introduction to artificial intelligence assignment

CS 188 | Introduction to Artificial Intelligence

Spring 2021, lectures: mon/wed/fri 3:00–3:59 pm, online.

Description

This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.

By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.

See the syllabus for slides, deadlines, and the lecture schedule. Readings refer to fourth edition of AIMA unless otherwise specified.

We make lecture and Q&A recordings available as links to Google Drive, which you can find posted together with other materials on the Syllabus page of this website shortly after the lecture. These links will work only if you are signed into your UC Berkeley Google account. The recordings are also available on Kaltura , which is a service that UC Berkeley partners with that facilitates the cloud recordings of Zoom meetings. All recordings on Kaltura have automatically-generated captions available by default alongside some other useful controls, such as playback speed adjustment.

To access the channel with recordings for this course, please go to this website and create an account if you don’t have one already: https://kaltura.berkeley.edu

Once you have the account, you should be able to access and subscribe to videos in the channel by following this link .

CS47100: Introduction to Artificial Intelligence (Spring 2023)

introduction to artificial intelligence assignment

Course Information

Artificial intelligence (AI) is about building intelligent machines that can perceive and act rationally to achieve their goals. To prepare students for this endeavor, we cover the following topics in this course: Search, constraint satisfaction, logic, reasoning under uncertainty, machine learning, and planning. There will be four assignments in the form of both written and programming problems.

Pre-requisites:

CS251 Data Structures (grade of C or better)
[AIMA] S. Russell and P. Norvig (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th Edition. (ISBN:9780134610993)
You can also use the 3rd edition and find the corresponding sections to read.
Assignments: 40% (10% each)
Midterm: 30%
Final Exam: 30%
Lecture slides and recordings will be posted on Brightspace.
The instructor & TAs can be best reached through Ed Discussion. Please post your questions there instead of emailing TAs.
During office hours or on Ed Discussion, please avoid posting partial homework solutions or asking TAs to "review" your code.
Zoom links for office hours are posted on Ed Discussion.
Tutorial for learning Latex with Overleaf: [Link]

Instructor & TAs

Raymond a. yeh.

Email: rayyeh [at] purdue.edu Office Hour: Mon 4-5PM Location: Zoom

Email: du286 [at] purdue.edu Office Hour: Thu 4-5PM Location: Zoom

Email: li4255 [at] purdue.edu Office Hour: Fri 10-11AM Location: Zoom

Email: li4178 [at] purdue.edu Office Hour: Wed 10-11AM Location: Zoom

Mir Imtiaz Mostafiz

Email: mmostafi [at] purdue.edu Office Hour: Fri 12PM-1PM Location: Zoom

Email: xinruw [at] purdue.edu Office Hour: Tue 10:30-11:30AM Location: Zoom

Ananya Singh

Email: singh745 [at] purdue.edu Office Hour: Mon 3-4PM Location: Zoom

Time & Location

Time: Mon. & Wed. (5:30 pm - 6:45 pm)
Location: Lilly Hall of Life Sciences G126

Other Resource

BrightSpace
Ed Discussion

Course Schedule

The following schedule is tentative and subject to change.

Late Policy

A 10% penalty will be applied (per day) to late assignments. Assignments that are more than two days late will not be accepted.

Academic Honesty

Please refer to Purdue's Student Guide for Academic Integrity . Academic dishonesty will result in an automatic zero on an assignment and your course grade will be reduced by one full letter grade. A second attempt will result in a failing grade for the course. It is one's responsibility to prevent others from copying your work.

Accessibility

Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, please contact the Disability Resource Center at: [email protected] or by phone at 765-494-1247 and the course instructor to arrange for accommodations.

Classroom Guidance Regarding Protect Purdue

Any student who has substantial reason to believe that another person is threatening the safety of others by not complying with Protect Purdue protocols is encouraged to report the behavior to and discuss the next steps with their instructor. Students also have the option of reporting the behavior to the Office of the Student Rights and Responsibilities . See also Purdue University Bill of Student Rights and the Violent Behavior Policy under University Resources in Brightspace.

University Policies

Please refer to additional university policies in BrightSpace .

Spring 2022

Note: this webpage is the one used in spring 2022 - it is not for winter 2024.

Dr. Jesse Hoey , ( jhoey [at] cs [dot] uwaterloo [dot] ca )

Communication

All communication should take place using the Piazza discussion board.
Sign up for Piazza (if you're not already) here.
Private Piazza posts (to instructors only) can be used for any posts that contain solution snippets or private questions.
Only in exceptional cases where you need to contact only the instructor should you use the personal email above.

Deliverables (Assignment submissions and grades)

Assignments and grades will be handled through Learn
If you are not familiar with Learn, see the instructions for using dropboxes to hand in assignments.
Midterm : Wednesday June 8th 7:00-8:50pm M3 1006
Final Exam : Monday, August 8th, 7:30pm-10:00pm (in person - M3 1006)
Jesse Hoey: TBA
TAs will hold special office hours for each assignment
Assignment 1 office hours (Zheng Ma and Kai Ma, TAs): May 24th 4pm-6pm (online with - link posted on Piazza)
Assignment 2 office hours (Dake Zhang and Qing Guo): June 13th, 3-5 PM. (online with - link posted on Piazza)

Primary Texts:

Secondary readings:, for cs486 students:.

Assignments (4) (40% - to be done individually - dates to be announced - see below for tentative dates).
One and a half hour written midterm examination (15% - Jun 8, 2022, 700pm-850pm in M3-1006).
Two and a half hour written final examination (Aug 8, 2022 730pm-1000pm in M3-1006) (45% and must pass the final to pass the course ).
Optional project (5% bonus) (see here for details).

For CS686 (grad) students:

Assignments (4) (25% - to be done individually - dates to be announced - see below for tentative dates).
One and a half hour written midterm examination (10% - Jun 8, 2022, 700pm-850pm in M3-1006).
Two and a half hour written final examination (Aug 8, 2022 730pm-1000pm in M3-1006) (35%).
Project (30%) (see here for details).

How and Where to submit

Assignments are to be done individually unless otherwise stated.
Submit assignments and receive marks through Learn .
No late assignments will be accepted.
Submit project proposals on LEARN before the midterm.
Students wishing to write a project (and all CS686 students) must submit a project proposal.
Submit final projects on LEARN before the final exam.

Course Objectives

The design of automated systems capable of accomplishing complicated tasks is at the heart of computer science. Abstractly, automated systems can be viewed as taking inputs and producing outputs towards the realization of some objectives. In practice, the design of systems that produce the best possible outputs can be quite challenging when the choice of outputs is constrained, the consequences of the outputs are uncertain and/or dependent on other systems, the information provided by the inputs is incomplete and/or noisy, there are multiple (possibly competing) objectives to satisfy, the system must adapt to its environment over time, etc. This course provides an introduction to Artificial Intelligence, covering some of the core topics that underly automated reasoning. The modeling techniques that will be covered are quite versatile and can be used to tackle a wide range of problems in many fields including natural language processing (e.g., topic modeling, document clustering), robotics (e.g., mobile robot navigation), automated diagnosis (e.g., medical diagnosis, fault detection), data mining (e.g., fraud detection, information retrieval), operations research (e.g., resource allocation, maintenance scheduling), assistive technologies, human-computer interaction, etc.

Course Topics

Agents and Abstraction
States and Searching
Features and Constraints
Propositions and Inference
Reasoning under uncertainty
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Machine Learning
Neural Networks and Deep Learning
Planning under certainty
Planning under uncertainty
Additional topics if time permits

COURSE SLIDES

May 3, 2022: Introduction Slides (88kb) ( 6-up version (118Kb) ) Readings: Poole and Mackworth (2nd Ed.) 1.1 video lecture
May 5, 2022: What is AI? Slides (49Mb) ( 6-up version (19Mb) ) Readings: Poole and Mackworth (2nd Ed.) 1.1-1.2 video lecture part 1 video lecture part 2
May 5, 2022: Agents and Abstraction Slides (6Mb) ( 6-up version (2Mb) ) Readings: Poole and Mackworth (2nd Ed.) 1.3-1.10, 2.1-2.3 video lecture part 1 video lecture part 2
May 10/12, 2022: States and Searching Slides (1Mb) ( 6-up version (1.7Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 3 (all) video lecture part 1 video lecture part 2 video lecture part 3
May 24, 2022: Propositions and Inference Slides (0.4Mb) ( 6-up version (1.5Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 5.1-5.3, and Chapt. 13.1-13.2 video lecture part 1 video lecture part 2 video lecture part 3
January 30th, 2020: AI Ethics (Guest Lecturer: Mathieu Doucet ) Slides (16Mb) Readings: Montreal Declaration for a responsible development of artificial intelligence and a short opinion piece by Yoshua Bengio (one of the AI researchers who developed the Declaration).
May 24/26th, 2022: Planning under certainty Slides (3Mb) ( 6-up version (3.1Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 6.1-6.4 video lecture
May 25th/31st, 2022: Supervised Learning I Slides (0.4Mb) ( 6-up version (1 Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 7.1-7.3.1,7.4 video lecture part 1 video lecture part 2 video lecture part 3
June 2-14, 2020: Reasoning under Uncertainty I Slides (1.5Mb) ( 6-up version (2.4 Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 8.1-8.4 video lecture part 1 video lecture part 2 video lecture part 3 video lecture part 4 video lecture part 5
February 18/20th, 2020: Reading week (no class)
June 16/21st: Reasoning under Uncertainty II Slides (3.1Mb) ( 6-up version (3.0 Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 8.5-8.9 video lecture part 1 video lecture part 2 video lecture part 3
June 23, 2022: Bayesian Learning Slides (0.25Mb) ( 6-up version (0.55 Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 10.1,10.4 video lecture part 1 video lecture part 2
June 28th, 2022: Supervised Learning under Uncertainty Slides (0.5Mb) ( 6-up version (1 Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 7.3.2,7.5-7.6 video lecture part 1 video lecture part 2
June 30th, 2022: Unsupervised Learning Slides (0.52Mb) ( 6-up version (0.7 Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 10.2,10.3,10.5 video lecture part 1 video lecture part 2
July 5-7 2022: Planning under Uncertainty I Slides (0.42Mb) ( 6-up version (0.89Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 9.1-9.3 video lecture part 1 and worked example 1 video lecture part 2 and worked example 2
July 12-14, 2022: Planning under Uncertainty II Slides (0.91Mb) ( 6-up version (1.52Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 9.5 video lecture part 1 and worked example 1 video lecture part 2 [OPTIONAL]
July 19, 2022: Planning under Uncertainty III Slides (3.3Mb) ( 6-up version (4.1 Mb) ) Readings: Poole and Mackworth (2nd Ed.) Chapt. 12.1,12.3-12.9 video lecture part 1 video lecture part 2
March 30th, 2020: Affective Computing and Social Dilemmas Slides (16Mb) ( 6-up version (16 Mb) ) Readings: none
April 1st, 2020: Recap Slides (68 Kb) ( 6-up version (160 Kb) ) Readings: none

ASSIGNMENTS

Assignment 1 Due May 25th, 2022
Assignment 2 Due Jun 15th, 2022
Assignment 3 Due July 4th, 2022
Assignment 4 Due July 20th, 2022

OTHER MATERIAL (videos, software, handouts, etc)

Geoff Hinton ACM Turing Award Lecture
Graph showing rise and fall of symbolic vs. connectionist AI.
Marcus-Bengio debate on symbolic vs. connectionist AI Dec 24th, 2019
Autonomous Intersection Management at UT Austin
Search Grid from lecture 3 as well as examples from class (depth, breadth, best, heuristic depth) and A*
Tic-Tac-Toe example from lecture 3
Crossword puzzle exercise from lecture 4
Pascal Van Hentenryck on transportation planning
paper on ride-sharing planning
Analysis of the Monty Python "witch" skit
A couple of simple Prolog programs: (propositional) and (with variables/first order) .
MU puzzle from Godel, Escher, Bach by Douglas R. Hofstadter.
Discussion Board example from lecture 7a.
A modern take on the bias-variance tradeoff . Also see this paper
A short introduction to the bias-variance tradeoff
solution to the MU puzzle
Variable elimination examples from lecture 8a.
Grumpy-Sad example for use with aispace Bayes' Net Applet
Cancer example for use with aispace Bayes' Net Applet
Robot Localization Problem from lecture 8b.
Stochastic Simulation examples from lecture 8b.
Note on partition function and P(A)/P*(A) (lecture 8b)
Max. Likelihood and EM for Naive Bayes derivation. (lecture 9a and 9c)
Bayesian learning (Candy example from lecture 9a)
David MacKay book (lecture 9a)
Derivation of Backpropagation Equations from lecture 9b.
Neal et al paper on bias variance in the overparameterized regime
EM exercise for simple Naive Bayes lecture 9c. Completed first iteration version is here
Decision making examples from lecture 10a
Coffee/Fridge/MacBook example from lecture 10a
Markov Decision Process Tutorial with examples covered in lecture 10b.
Reinforcement Learning Tutorial with examples covered in lecture 10c
RL book (Sutton and Barto)
Deep Q-Network for Atari games - Mnih et al. Nature 2015.
Yoshua Bengio NeurIPS 2020 talk on Deep Learning
Bengio-Marcus Debate : December 24th, 2020
Pascal Van Hentenryck talk on Disaster Recovery
Geoff Hinton speaking about connectionist vs. symbolic AI .
AI Magazine article on Watson
Cancer example decision network for use with aispace Bayes' Net Applet. From lecture 10a (march 18th). Second version where the "Do Test B" decision conditions the "Test B" node rather than the "Database" node.
Studentbot applet (in zip file) or online (if your browser still supports this)
Mastering Go with MCTS and neural networks - AlphaGo Zero - Silver et al. Nature 2017.
talk on affect control theory
Trevor Bekolay neural networks lecture notes Section 1 , Section 2 .
Character recognition demo (convolutional networks)
Deep Learning
Deep learning site
Coffee/Kubota/Impreza example
Decision forests software
Bayesian affect control theory website
Affect Control theory (original) website
Cancer example decision network for use with aispace Bayes' Net Applet. From lecture 10a (march 12th). Second version where the "Do Test B" decision conditions the "Test B" node rather than the "Database" node.
Decision making examples from lecture 10a (March 12th)
EM exercise for simple Naive Bayes (class March 7th). Completed first iteration version is here
Bayesian learning (Candy example from class)
David MacKay book
Derivation of Backpropagation Equations from lecture 7b.
Mu puzzle from Godel, Escher, Bach by Douglas R. Hofstadter. See solution to the puzzle
Economic reasoning and AI paper by David C. Parkes and Michael P. Wellman.
Dieter Fox Particle Filtering Examples for Localisation
Rod Brooks talk on usable robots

University of Waterloo Academic Integrity Policy

The University of Waterloo Senate Undergraduate Council has also approved the following message outlining University of Waterloo policy on academic integrity and associated policies.

Academic Integrity

In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. Check the Office of Academic Integrity's website for more information. All members of the UW community are expected to hold to the highest standard of academic integrity in their studies, teaching, and research. This site explains why academic integrity is important and how students can avoid academic misconduct. It also identifies resources available on campus for students and faculty to help achieve academic integrity in, and our, of the classroom.

A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70 - Student Petitions and Grievances, Section 4. When in doubt please be certain to contact the department's administrative assistant who will provide further assistance.

A student is expected to know what constitutes academic integrity, to avoid committing academic offenses, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offense, or who needs help in learning how to avoid offenses (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course professor, academic advisor, or the Undergraduate Associate Dean. For information on categories of offenses and types of penalties, students should refer to Policy 71-Student Discipline . For typical penalties check Guidelines for the Assessment of Penalties .

Avoiding Academic Offenses

Most students are unaware of the line between acceptable and unacceptable academic behaviour, especially when discussing assignments with classmates and using the work of other students. For information on commonly misunderstood academic offenses and how to avoid them, students should refer to the Faculty of Mathematics Cheating and Student Academic Discipline Policy.

A decision made or a penalty imposed under Policy 70, Student Petitions and Grievances (other than a petition) or Policy 71, Student Discipline may be appealed if there is a ground. A student who believes he/she has a ground for an appeal should refer to Policy 72 - Student Appeals .

Note for students with disabilities

The AccessAbility Services Office (AAS), located in Needles Hall, Room 1401, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with the AAS at the beginning of each academic term.

Modules (videos and slides) : We will have an intro lecture to the week's material on Monday during class time. The rest of the week's material will be delivered through modules, pre-recorded course videos that students can watch at their own time. Each week's modules are listed in the schedule and can be accessed here .
Problem sessions: During our class time each Wednesday, we will hold a problem session where a CA will guide students to work together through practice problems.
Study halls & homework parties: We will hold weekly study halls and homework parties for students to get together remotely and study or work on homework together. CAs will be present to answer any questions. We encourage students to come for these over 1:1 office hours, since other students might benefit from hearing your questions as well.
Open OH : These are open to all students and require no appointment.
1:1 OH : These will be appointment-based so that students need not wait in the queue. See our calendar for times and sign up links. Please contact us through Ed if you want to schedule one but cannot find an available slot.
Homework OH : Office hours designated specifically for questions about the homework.
General OH : Office hours where the CAs answer all non-homework questions, including those about modules, exams, and projects.

We are also making three changes to the course structure:

Shorter Exams: To lower the stress of having a single high-stakes assessment, we will have a shorter midterm instead of a 3-hour one.
Modules: Each week's modules will be posted in our schedule at the start of the week.
Live Lectures (Zoom): Mondays 1:00-2:20pm (class time).
Problem Sessions (Nooks): Wednesdays 1:00-2:20pm (class time).
Study Halls (Nooks): Tuedays 5-7pm, Thursdays 7-8pm, Fridays 9-10am.
Homework Parties (Nooks): Wednesdays 5-7pm, Saturdays 10am-12pm.
Office Hours (Nooks): Throughout the week; sign up links are on our class calendar .
Programming ( CS 106A , CS 106B , CS 107 )
Discrete math ( CS 103 )
Probability ( CS 109 )
Linear algebra (Math 51)
Russell and Norvig. Artificial Intelligence: A Modern Approach. A comprehensive reference for all the AI topics that we will cover.
Koller and Friedman. Probabilistic Graphical Models. Covers factor graphs and Bayesian networks (this is the textbook for CS228).
Sutton and Barto. Reinforcement Learning: An Introduction. Covers Markov decision processes and reinforcement learning. Available free online.
Hastie, Tibshirani, and Friedman. The elements of statistical learning. Covers machine learning. Available free online.
Tsang. Foundations of constraint satisfaction. Covers constraint satisfaction problems. Available free online.

With Project Homeworks : 50%. Exams : 30%. Project : 20%. Without Project Homeworks : 60%. Exams : 40%.

Exam 1 Date : released at 2:30PT on Wednesday, May 5 ; due at 2:30PT on Thursday, May 6 . Length : 100 minutes. Designed to be completed within 80 minutes, with an additional 20-minute leeway for scanning and/or uploading. Format : The exam will be open book and distributed and administered through Gradescope and will be available for 24 hours. Students can choose any block of time within the designated window above to take the exam. Exam 2 Date : released at 2:30PT on Wednesday, June 2 ; due at 2:30PT on Thursday, June 3 . Length : 120 minutes. Designed to be completed within 80 minutes, with an additional 40-minute leeway for scanning and/or uploading. Format : Same as Exam 1. Conflict : If you have a major time conflict for either exam (e.g., an academic conference), you should let us know privately via email by Friday, January 29 (week 3).

Project interest form [p-interest] (due Fri April 16 )
Project proposal [p-proposal] (due Fri April 30 )
Project progress report [p-progress] (due Fri May 21 )
Project final report and video [p-final] (due Fri June 4 )

Regardless of the group size, all groups must submit the work detailed in each milestone and will be graded on the same criteria. Although we allow 1-2 person project groups, we encourage groups of 3-4 members. We encourage teams of 3-4 students because this size typically best fits the expectations for CS 221 projects. We expect each team to submit a completed project (even for team of 1 or 2). All projects require that students spend time gathering data, and setting up the infrastructure to reach an end result. A 3 or 4 person team can share these tasks much better, allowing the team to focus more on the interesting results and discussion in the project. Each member of the team should contribute in both technical and non-technical components of the project. We will provide resources on Ed and the project page that can help you find group members.

Extra Credit : You will be awarded with up to 3% extra credit based on your contributions to the class on Ed.

The programming assignments are designed to be run in GNU/Linux environments. Most or all of the grading code may incidentally work on other systems such as MacOS or Windows, and students may optionally choose to do most of their development in one of these alternative environments. However, no technical support will be provided for issues that only arise on an alternative environment. Moreover, no matter what environment is used during development, students must confirm that their code (specifically, the student's submission.py ) runs on Gradescope .

The original grader.py script (operating on the submitted submission.py ) may not exit normally if you use calls such as quit() , exit() , sys.exit() , and os._exit() . Also note that Python packages outside the standard library are not guaranteed to work. Therefore, do not use packages like numpy, scikit-learn, and pandas .
The code reads external resources other than the files given in the assignment.
The code is malicious. This is considered a violation of the honor code. The score of the assignment will be zero (0) and the incident will be reported to the Office of Judicial Affairs.
Looking at the writeup or code of another student.
Showing your writeup or code to another student.
Discussing homework problems in such detail that your solution (writeup or code) is almost identical to another student's answer.
Uploading your writeup or code to a public repository (e.g. github, bitbucket, pastebin) so that it can be accessed by other students.
Looking at solutions from previous years' homeworks - either official or written up by another student.

For assignments with a programming component, we will automatically sanity check your code in some basic test cases, but we will grade your code on additional test cases. Important : just because you pass the basic test cases, you are by no means guaranteed to get full credit on the other, hidden test cases, so you should test the program more thoroughly yourself!

Unless the assignment instructs otherwise, all of your code modifications should be in submission.py and all of your written answers in <assignment ID>.pdf . Upload the former to Gradescope under the "Programming" section, and the latter under the "Written" section.

Nooks links included in the schedule below assume you have already signed into our Nooks space following our class specific link shared on Canvas and Ed.

Lectures: Mon/Wed 10:30am-12:00pm in NVIDIA Auditorium .
Problem sessions: Fri 10:30am-12:00pm in NVIDIA Auditorium .
Office hours, homework parties: see the Calendar and the HW OH In Person Queue and Online Queue .
Try our new LLM powered bot on slack . Note: do not direct message any members of course staff on Slack.
To contact all teaching staff, use Ed .
For personal/sensitive matters, email [email protected] .
Modules : All the course content has been broken up into short modules , which include slides, recorded videos, and notes.
Lectures: Instructors go over the main modules more slowly and interactively. All lectures will be recorded and available on Canvas.
Problem sessions: CAs work through practice homework and exam problems.
Homework parties : CAs help students work through homework problems in small groups.
Office Hours: Meet 1:1 with instructors and CAs. There are two types of CA office hours: homework OH (for help with homework questions) and general OH (to ask questions about course content from lecture).
Looking at the writeup or code of another student.
Showing your writeup or code to another student.
Discussing homework problems in such detail that your solution (writeup or code) is almost identical to another student's solution.
Uploading your writeup or code to a public repository (e.g., GitHub) so that it can be accessed by other students.
Looking at solutions from previous years, either official or written up by another student, or found online.

Generative AI Policy: Each student is expected to submit their own solutions to the CS221 homeworks. You may use generative AI tools such as Co-Pilot and ChatGPT as you would use a human collaborator. This means that you may not directly ask generative AI tools for answers or copy solutions, and acknowledge generative AI tools as collaborators. The use of generative AI tools to substantially complete an assignment or exam (e.g. by directly copying) is prohibited and will result in honor code violations. We will be checking students' homework to enforce this policy.

Anyone violating the honor code policy will be referred to the Office of Judicial Affairs. If you think you violated the policy (it can happen, especially under time pressure!), please reach out to us; the consequences will be much less severe than if we approach you.

Note that messages on public channels in slack are visible to other students and course staff.
It is a strict violation of course policies to direct message course staff on slack, please keep interaction to public threads or reach out via Ed or the lead staff mailing list if you have questions.
The student honor code still applies to messages and interactions on slack.
(Required) Programming CS 106A , CS 106B
(Required) Discrete math, mathematical rigor: CS 103
(Required) Probability: CS 109
(Required) Linear algebra: Math 51
(recommended, but not required) Algorithms: CS 161
(recommended, but not required) Systems: CS 107
Russell and Norvig. Artificial Intelligence: A Modern Approach. A comprehensive reference for all the AI topics that we will cover.
Koller and Friedman. Probabilistic Graphical Models. Covers factor graphs and Bayesian networks (this is the textbook for CS228 ).
Sutton and Barto. Reinforcement Learning: An Introduction. Covers Markov decision processes and reinforcement learning (free online).
Hastie, Tibshirani, and Friedman. The Elements of Statistical Learning. Covers machine learning from a rigorous statistical perspective (free online).
Tsang. Foundations of Constraint Satisfaction. Covers constraint satisfaction problems (free online).
Prerequisites Quiz (0.5%): There is a quiz on the prerequisites that you must take within 1 week of the start of the term. The quiz is on Gradescope and is open book. You have unlimited attempts and no time limit, and the quiz offers study materials and explanations to help with review.
Exam 1 (29.5%): May 8th, 6-8 PM on Campus .
Exam 2 (30%): June 7th, 3:30 - 5:30 PM on Campus .
If you have a major time conflict for either exam, you should fill out this form by Friday, October 13 (week 3) .

Both exams will be in-person.

SCPD Students: SCPD students will need to nominate exam monitors for both exams and coordinate the exam process with the SCPD exams team . Please refer to this link for more information on the process. For any additional questions, please reach out to the SCPD exams team .

Projects should be done in groups of 1-4 students.
There are 5 milestones for the project throughout the quarter: interest form, proposal, progress report, video/poster, final report.
Each project group will be assigned a CA mentor who will give feedback and answer questions.
For inspiration, check out previous CS221 projects .
See the project page for more details.
Project interest form [p-interest] (due Fri Apr 19 )
Project proposal [p-proposal] (due Fri May 3 )
Project progress report [p-progress] (due Fri May 24 )
Project final report and video [p-final] (due Mon Jun 3 )
Ed (up to 2% extra credit): Please help answer your classmates's questions on Ed! You will be awarded extra credit depending on how substantial and helpful you were on Ed.

Computer Science and Engineering
NOC:An Introduction to Artificial Intelligence (Video)
Co-ordinated by : IIT Delhi
Available from : 2019-11-13
Intro Video
Introduction: What to Expect from AI
Introduction: History of AI from 40s - 90s
Introduction: History of AI in the 90s
Introduction: History of AI in NASA & DARPA(2000s)
Introduction: The Present State of AI
Introduction: Definition of AI Dictionary Meaning
Introduction: Definition of AI Thinking VS Acting and Humanly VS Rationally
Introduction: Definition of AI Rational Agent View of AI
Introduction: Examples Tasks, Phases of AI & Course Plan
Uniform Search: Notion of a State
Uniformed Search: Search Problem and Examples Part-2
Uniformed Search: Basic Search Strategies Part-3
Uniformed Search: Iterative Deepening DFS Part-4
Uniformed Search: Bidirectional Search Part-5
Informed Search: Best First Search Part-1
Informed Search: Greedy Best First Search and A* Search Part-2
Informed Search: Analysis of A* Algorithm Part-3
Informed Search Proof of optimality of A* Part-4
Informed Search: Iterative Deepening A* and Depth First Branch & Bound Part-5
Informed Search: Admissible Heuristics and Domain Relaxation Part-6
Informed Search: Pattern Database Heuristics Part-7
Local Search: Satisfaction Vs Optimization Part-1
Local Search: The Example of N-Queens Part-2
Local Search: Hill Climbing Part-3
Local Search: Drawbacks of Hill Climbing Part-4
Local Search: of Hill Climbing With random Walk & Random Restart Part-5
Local Search: Hill Climbing With Simulated Anealing Part-6
Local Search: Local Beam Search and Genetic Algorithms Part-7
Adversarial Search : Minimax Algorithm for two player games
Adversarial Search : An Example of Minimax Search
Adversarial Search : Alpha Beta Pruning
Adversarial Search : Analysis of Alpha Beta Pruning
Adversarial Search : Analysis of Alpha Beta Pruning (contd...)
Adversarial Search : Horizon Effect, Game Databases & Other Ideas
Adversarial Search: Summary and Other Games
Constraint Satisfaction Problems: Representation of the atomic state
Constraint Satisfaction Problems: Map coloring and other examples of CSP
Constraint Satisfaction Problems: Backtracking Search
Constraint Satisfaction Problems: Variable and Value Ordering in Backtracking Search
Constraint Satisfaction Problems: Inference for detecting failures early
Constraint Satisfaction Problems: Exploiting problem structure
Logic in AI : Different Knowledge Representation systems - Part 1
Logic in AI : Syntax - Part - 2
Logic in AI : Semantics - Part - 3
Logic in AI : Forward Chaining - Part 4
Logic in AI : Resolution - Part - 5
Logic in AI : Reduction to Satisfiability Problems - Part - 6
Logic in AI : SAT Solvers : DPLL Algorithm - Part - 7
Logic in AI : Sat Solvers: WalkSAT Algorithm - Part - 8
Uncertainty in AI: Motivation
Uncertainty in AI: Basics of Probability
Uncertainty in AI: Conditional Independence & Bayes Rule
Bayesian Networks: Syntax
Bayesian Networks: Factoriziation
Bayesian Networks: Conditional Independences and d-Separation
Bayesian Networks: Inference using Variable Elimination
Bayesian Networks: Reducing 3-SAT to Bayes Net
Bayesian Networks: Rejection Sampling
Bayesian Networks: Likelihood Weighting
Bayesian Networks: MCMC with Gibbs Sampling
Bayesian Networks: Maximum Likelihood Learning"
Bayesian Networks: Maximum a-Posteriori LearningÃÂ
Bayesian Networks: Bayesian Learning
Bayesian Networks: Structure Learning and Expectation Maximization
Introduction, Part 10: Agents and Environments
Decision Theory: Steps in Decision Theory
Decision Theory: Non Deterministic Uncertainty
Probabilistic Uncertainty & Value of perfect information
Expected Utility vs Expected Value
Markov Decision Processes: Definition
Markov Decision Processes: An example of a Policy
Markov Decision Processes: Policy Evaluation using system of linear equations
Markov Decision Processes: Iterative Policy Evaluation
Markov Decision Processes: Value Iteration
Markov Decision Processes: Policy Iteration and Applications & Extensions of MDPs
Reinforcement Learning: Background
Reinforcement Learning: Model-based Learning for policy evaluation (Passive Learning)
Reinforcement Learning: Model-free Learning for policy evaluation (Passive Learning)
Reinforcement Learning: TD Learning
Reinforcement Learning: TD Learning and Computational Neuroscience
Reinforcement Learning: Q Learning
Reinforcement Learning: Exploration vs Exploitation Tradeoff
Reinforcement Learning: Generalization in RL
Deep Learning : Perceptrons and Activation functions
Deep Learning : Example of Handwritten digit recognition
Deep Learning : Neural Layer as matrix operations
Deep Learning : Differentiable loss function
Deep Learning : Backpropagation through a computational graph
Deep Learning : Thin Deep Vs Fat Shallow Networks
Deep Learning : Convolutional Neural Networks
Deep Learning : Deep Reinforcement Learning
Ethics of AI : Humans vs Robots
Ethics of AI : Robustness and Transparency of AI systems
Ethics of AI : Data Bias and Fairness of AI systems
Ethics of AI : Accountability, privacy and Human-AI interaction
Watch on YouTube
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Transcripts

Introduction to Artificial Intelligence

This unit is designed to give students a broad outline of algorithmic problem solving and the basic concepts of artificial intelligence. It is assumed that students already have good programming skills in at least one of the programming languages Java/C/C++.

OR COS30008 Data Structures and Patterns

Assumed Knowledge Object oriented programming at an intermediate level

Learning outcomes

Students who successfully complete this unit will be able to:

Describe and interpret the fundamental concepts of Artificial Intelligence (AI) and generic problem solving techniques
Apply advanced algorithms and data structures to solve common problems
Design software that implements AI concepts

Teaching methods

All applicable locations.

Introduction to Artificial Intelligence and Intelligent Agents
Introduction to Logic and Reasoning
Uninformed and Informed Search
Knowledge Representation
Expert Systems
AI Planning
Uncertain Knowledge and Reasoning
Decision Making with Uncertainty
Adaptation and Machine Learning
Philosophical Aspects of AI

Study resources

Reading materials.

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.

Review Assignment
Announcements
About the Course
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Important Notice:No CHANGE in NPTEL Exam Schedule for April 2024

Dear Student,

We wanted to take a moment to address an important matter regarding the upcoming election dates and their potential impact on your exam schedule.

None of the election dates clash with scheduled exam dates. If we schedule additional dates, we will ensure they again do not clash with elections in your state.
Hence this is to confirm that there will be no changes to the exam dates and they are the same as previously scheduled. We may have exams in some cities on April 19 and April 26 depending on seat availability on scheduled dates. But again this will be done ensuring we don't conduct exams on election dates in your state.
Your academic progress and success remain our top priority, and we are committed to maintaining the integrity of the examination process.
We have more than 6 lakh learners registered for April exams and logistics has been a huge challenge. We understand that some of you may need to travel to your native cities to participate in the voting process. Please remember that you selected your exam cities during registration, and it is crucial that you return to these cities to take your exams as scheduled. Since hall ticket and center allocation is under process, exam cities selected by you during exam registration cannot be changed now.

Hence we kindly request that you make the necessary arrangements to ensure you can both exercise your right to vote and fulfill your academic obligations.

Warm Regards,

NPTEL Team.

NPTEL: Exam Registration date is extended for 12 week courses of Jan 2024!

No further extension will be provided.
This extension is only applicable for 12-week courses.

Reminder: NPTEL: Exam Registration is date is extended for Jan 2024 courses!

Dear Learner, The exam registration for the Jan 2024 NPTEL course certification exam is extended till February 23, 2024 - 05.00 P.M . CLICK HERE to register for the exam Choose from the Cities where exam will be conducted: Exam Cities Click here to view Timeline and Guideline : Guideline For further details on registration process please refer the previous announcement in the course page dated January 30, 2024. -NPTEL Team

NPTEL: Exam Registration is open now for Jan 2024 courses!

Dear Candidate,

Here is a golden opportunity for those who had previously enrolled in this course during the Jan 2023 semester, but could not participate in the exams or were absent/did not pass the exam for this course. This course is being reoffered in Jan 2024 and we are giving you another chance to write the exam in April 20, 2024 a nd obtain a certificate based on NPTEL norms. Do not let go of this unique opportunity to earn a certificate from the IITs/IISc.

IMPORTANT instructions for learners - Please read this carefully

1. The exam date for this course: April 20, 2024

2. CLICK HERE to register for the exam.

Please fill the exam form using the same Enrolled email id & make fee payment via the form, as before.

3. Choose from the Cities where exam will be conducted: Exam Cities

4. You DO NOT have to re-enroll in the courses.

5. You DO NOT have to resubmit Assignments OR participate in the non-proctored programming exams(if applicable) in the previous semester

6. If you do enroll in the Jan 2024 course, we will take the best average assignment scores/non-proctored programming exam(if applicable) score across the two semesters.

Please check once if you have >= 40/100 in average assignment score and also participated and satisfied the criteria in the non-proctored programming exams(if applicable) that were conducted in Jan 2023 to become eligible for the e-certificate, wherever applicable.

If not, please submit assignments again in the Jan 2024 course and also participate in the non-proctored programming exams(if applicable) to become eligible for the e-certificate.

We will not be having new assignments or unproctored exams(if applicable) in the previous semester's (Jan 2023) course.

RECOMMENDATION: If you want to take new assignments and an unproctored exam(if applicable) or brush up on your lessons for the exam, please enroll in the Jan 2024 course.

Click here to enroll in the current course, links are provided corresponding to the course name.

7. Exam fees:

If you register for the exam and pay before March 11, 2024 - 5:00 PM, Exam fees will be Rs. 1000/- per exam .

8. 50% fee waiver for the following categories:

Students belonging to the SC/ST category: please select Yes for the SC/ST option and upload the correct Community certificate.

Students belonging to the PwD category with more than 40% disability: please select Yes for the option and upload the relevant Disability certificate.

9. Last date for exam registration: March 15, 2024 - 5:00 PM (Friday).

10. Between March 11, 2024 - 5:00 PM & March 15, 2024 - 5:00 PM late fee will be applicable.

11. Mode of payment: Online payment - debit card/credit card/net banking/UPI.

12. HALL TICKET:

The hall ticket will be available for download tentatively by 2 weeks prior to the exam date. We will confirm the same through an announcement once it is published.

13. FOR CANDIDATES WHO WOULD LIKE TO WRITE MORE THAN 1 COURSE EXAM:- you can add or delete courses and pay separately – till the date when the exam form closes. Same day of exam – you can write exams for 2 courses in the 2 sessions. Same exam center will be allocated for both the sessions.

14. Data changes:

Last date for data changes: March 15, 2024 - 5:00 PM :

We will charge an additional fee of Rs. 200 to make any changes related to name, DOB, photo, signature, SC/ST and PWD certificates after the last date of data changes.

The following 6 fields can be changed (until the form closes) ONLY when there are NO courses in the course cart. And you will be able to edit those fields only if you: -

REMOVE unpaid courses from the cart And/or - CANCEL paid courses

1. Do you come under the SC/ST category? *

2. SC/ST Proof

3. Are you a person with disabilities? *

4. Are you a person with disabilities above 40%?

5. Disabilities Proof

6. What is your role?

Note: Once you remove or cancel a course, you will be able to edit these fields immediately.

But, for canceled courses, refund of fees will be initiated only after 2 weeks.

15. LAST DATE FOR CANCELING EXAMS and getting a refund: March 15, 2024 - 5:00 PM

16. Click here to view Timeline and Guideline : Guideline

Domain Certification

Domain Certification helps learners to gain expertise in a specific Area/Domain. This can be helpful for learners who wish to work in a particular area as part of their job or research or for those appearing for some competitive exam or becoming job ready or specialising in an area of study.

Every domain will comprise Core courses and Elective courses. Once a learner completes the requisite courses per the mentioned criteria, you will receive a Domain Certificate showcasing your scores and the domain of expertise. Kindly refer to the following link for the list of courses available under each domain: https://nptel.ac.in/domains

Outside India Candidates

Candidates who are residing outside India may also fill the exam form and pay the fees. Mode of exam and other details will be communicated to you separately.

Thanks & Regards,

Thank you for learning with NPTEL!!

Dear Learner, Thank you for taking the course with NPTEL!! Hope you enjoyed the journey with us. The results for this course have been published and we are closing this course now. You will still have access to the contents and assignments of this course, if you click on the course name from the "Mycourses" tab on swayam.gov.in. For any further queries please write to [email protected] . - Team NPTEL

An Introduction to Artificial Intelligence : Result Re-Published!!

***THIS IS APPLICABLE ONLY FOR EXAM REGISTERED CANDIDATES*** ****Please don't click on below link, if you are not registered/not present for the Exam**** Dear Candidate, The exam scores and E Certificates have been Re-Published for April 2023 Exam(s). Step 1 - Are the results of my courses released? Please check the Results Re-Published courses list in the below links.:- Apr 2023 Exam - Click here Step 2 - How to check Results? Please login to internalapp.nptel.ac.in/ . and check your exam results. Use the same login credentials as used to register to the exam. What's next? Please read the pass criteria carefully and check against what you have gotten. If you still have any issues, please report the same here. internalapp.nptel.ac.in/ . We will reply within a week. Last date to report queries: 3 days within publishing of scores. Note : Hard copies of certificates will not be dispatched. The duration shown in the certificate will be based on the timeline of offering of the course in 2023, irrespective of which Assignment score that will be considered. Thanks and Best wishes. NPTEL Team

An Introduction to Artificial Intelligence : Result Published!!

***THIS IS APPLICABLE ONLY FOR EXAM REGISTERED CANDIDATES*** ****Please don't click on below link, if you are not registered/not present for the Exam**** Dear Candidate, The exam scores and E Certificates have been released for April 2023 Exam(s). Step 1 - Are the results of my courses released? Please check the Results published courses list in the below links.:- Apr 2023 Exam - Click here Step 2 - How to check Results? Please login to internalapp.nptel.ac.in/ . and check your exam results. Use the same login credentials as used to register to the exam. What's next? Please read the pass criteria carefully and check against what you have gotten. If you still have any issues, please report the same here. internalapp.nptel.ac.in/ . We will reply within a week. Last date to report queries: 3 days within publishing of scores. Note : Hard copies of certificates will not be dispatched. The duration shown in the certificate will be based on the timeline of offering of the course in 2023, irrespective of which Assignment score that will be considered. Thanks and Best wishes. NPTEL Team

Survey on Problem Solving sessions - An Introduction to Artificial Intelligence - (noc23-cs05)

Dear Learners, We would like to know if the expectations with which you attended this problem solving session are being met and hence please do take 2 minutes to fill out our feedback form. It would help us tremendously in gauging the learner experience. Here is the link to the form: https://docs.google.com/forms/d/15s4XrL2icAsMddCZL1F35F03zB6LlhGgDKGB4yq9Vhc/viewform -NPTEL TEAM

An Introduction to Artificial Intelligence : Final Feedback Form !!!

Dear students, We are glad that you have attended the NPTEL online certification course. We hope you found the NPTEL Online course useful and have started using NPTEL extensively. In this regard, we would like to have feedback from you regarding our course and whether there are any improvements, you would like to suggest. We are enclosing an online feedback form and would request you to spare some of your valuable time to input your observations. Your esteemed input will help us in serving you better. The link to give your feedback is: https://docs.google.com/forms/d/1ugIkHUFTfdm8qdfcg5YfKtfEiVUVatr-Z22vZUdYRHk/viewform We thank you for your valuable time and feedback. Thanks & Regards, -NPTEL Team

April 2023 NPTEL Exams - Hall Tickets Released!

***THIS IS APPLICABLE ONLY FOR EXAM REGISTERED CANDIDATES*** ****Please don't click on below link, if you are not registered for the Exam**** Dear Candidate, Your Hall Ticket / admit card for the NPTEL Exam(s) in April, 2023 has been released. Please login to https://internalapp.nptel.ac.in/ using your exam registered email id and download your hall ticket. Note: Requests for changes in exam city, exam center, exam date, session, or course will NOT be entertained. Please write to [email protected] for any further queries. All the best for your exams! Warm Regards NPTEL Team

An Introduction to Artificial Intelligence : Problem solving Session Reminder !!

Re-evaluation for the course "An Introduction to Artificial Intelligence"

Dear Student; Re-evaluation has been done by changing the answer for Question 7 in Assignment 10. Students are requested to find their revised scores of Assignment 10 in the Progress page.

Exam Format - April, 2023 !!

Dear Candidate, ****This is applicable only for the exam registered candidates**** Type of exam will be available in the list: Click Here You will have to appear at the allotted exam center and produce your Hall ticket and Government Photo Identification Card (Example: Driving License, Passport, PAN card, Voter ID, Aadhaar-ID with your Name, date of birth, photograph and signature) for verification and take the exam in person. You can find the final allotted exam center details in the hall ticket. The hall ticket is yet to be released. We will notify the same through email and SMS. Type of exam: Computer based exam (Please check in the above list corresponding to your course name) The questions will be on the computer and the answers will have to be entered on the computer; type of questions may include multiple choice questions, fill in the blanks, essay-type answers, etc. Type of exam: Paper and pen Exam (Please check in the above list corresponding to your course name) The questions will be on the computer. You will have to write your answers on sheets of paper and submit the answer sheets. Papers will be sent to the faculty for evaluation. On-Screen Calculator Demo Link: Kindly use the below link to get an idea of how the On-screen calculator will work during the exam. https://tcsion.com/ OnlineAssessment/ ScientificCalculator/ Calculator.html NOTE: Physical calculators are not allowed inside the exam hall. Thank you! -NPTEL Team

Weekly Feedback Form is uploaded for the course "An Introduction to Artificial Intelligence"

Dear student, Please note that there is a Weekly feedback form to be filled by you and the Form has been created under each unit in every week of the course page. We value your feedback and wish to know how you found the videos and the questions asked - whether they were easy, difficult, as per your expectations, etc We shall use this to make the course better and we can also know from the feedback which concepts need more explanation, etc. Here is the link to the form: https://docs.google.com/forms/d/1MIorG_qIz-GP7swfT_W2sufpX13vfQXy8sbhCxO9XeM/viewform

An Introduction to Artificial Intelligence : Week 12 content is live now

Dear Students, The lecture videos for Week 12 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=122&lesson=123 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment-12 for Week-12 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=122&assessment=190 The assignment has to be submitted on or before Wednesday,[19/04/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

An Introduction to Artificial Intelligence : Week 11 content is live now

Dear Students, The lecture videos for Week 11 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=112&lesson=113 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment-11 for Week-11 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=112&assessment=189 The assignment has to be submitted on or before Wednesday,[12/04/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

Solutions uploaded for the course "An Introduction to Artificial Intelligence"

Dear students The Assignment Solution have been uploaded for the course [An Introduction to Artificial Intelligence]. The Solution can be accessed using the following link: Week 2 - https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=180&lesson=181 Week 3 - https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=180&lesson=182 Week 4 - https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=180&lesson=183 Week 5 - https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=180&lesson=184 Week 6 - https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=180&lesson=185 Week 7 - https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=180&lesson=186 Week 8 - https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=180&lesson=187 Week 9 - https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=180&lesson=188

An Introduction to Artificial Intelligence : Week 10 content is live now

Dear Students, The lecture videos for Week 10 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=100&lesson=101 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment-10 for Week-10 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=100&assessment=179 The assignment has to be submitted on or before Wednesday,[05/04/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

Potential additional date (April 28th) for the April 2023 NPTEL exams

***THIS IS APPLICABLE ONLY FOR EXAM REGISTERED CANDIDATES*** Dear Student, Greetings from NPTEL! The Jan 2023 session is coming to an end soon and it’s time to put all your best efforts for the certification exam. The NPTEL team has always been there to make your learning process a joyful one and we hope that with your support we will be able to overcome the challenges of conducting a nation-wide exam of this magnitude. With the closure of exam registration form for the NPTEL April 2023 exams, the final registration count stands at 5.1 Lakh compared to 3.7 Lakh reported during the Jul-Dec 2022 semester. Based on the previous semester data, seats at the certification exam centres are booked by NPTEL at the beginning of the exam registration process. As the semester progresses, sometimes these numbers exceed our estimate, especially on certain dates and certain exam cities. Our goal is to allocate the chosen exam cities to all our learners. And by and large, we are able to allocate the chosen cities and dates with active support from our exam partner and our partner colleges. All efforts are being made to allocate the city of choice or the next nearest exam city for the April 29th/30th 2023 exams, as scheduled. However, in view of the unexpectedly large volume of exam registrations & limitation of seats at certain cities on a particular date, we may be compelled to shift the exam date of certain candidates to 28th April 2023 (Friday) as a last resort, only after exhausting all possibilities. We hope that you will appreciate our logistical constraints of such rescheduling and extend all necessary support as before and participate in the exam. With best wishes for the forthcoming exam(s), Warm regards, NPTEL Team

An Introduction to Artificial Intelligence : Week 9 content is live now

Dear Students, The lecture videos for Week 9 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=90&lesson=91 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment-9 for Week-9 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=90&assessment=178 The assignment has to be submitted on or before Wednesday,[29/03/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: March 17, 2023 - Friday Time: 07.00 PM - 09.00 PM Link to join: https://meet.google.com/hny-bzpv-ckp Session 2 : Due to unavoidable circumstances, The Problem solving Session organized tomorrow ( March 17, 2023 - Friday)( 06.00 PM - 08.00 PM) is Postponed to March 21, 2023 - Tuesday . The G-meet link for the session will be shared before the session. -NPTEL Team

We would like to know if the expectations with which you attended this problem solving session are being met and hence please do take 2 minutes to fill out our feedback form. It would help us tremendously in gauging the learner experience. Here is the link to the form: https://docs.google.com/forms/d/15s4XrL2icAsMddCZL1F35F03zB6LlhGgDKGB4yq9Vhc/viewform

An Introduction to Artificial Intelligence : Week 8 content is live now

Dear Students, The lecture videos for Week 8 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=80&lesson=81 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment-8 for Week-8 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=80&assessment=177 The assignment has to be submitted on or before Wednesday,[22/03/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

An Introduction to Artificial Intelligence : Week 7 content is live now

Dear Students, The lecture videos for Week 7 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=70&lesson=71 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment-7 for Week-7 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=70&assessment=176 The assignment has to be submitted on or before Wednesday,[15/03/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

An Introduction to Artificial Intelligence : Week 6 content is live now

Dear Students, The lecture videos for Week 6 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=62&lesson=63 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment-6 for Week-6 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=62&assessment=175 The assignment has to be submitted on or before Wednesday,[08/03/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

An Introduction to Artificial Intelligence : Week 5 content is live now

Dear Students, The lecture videos for Week 5 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=53&lesson=54 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment-5 for Week-5 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=53&assessment=174 The assignment has to be submitted on or before Wednesday,[01/03/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

An Introduction to Artificial Intelligence : Problem solving Session Cancellation!!

Dear learner, Due to unavoidable circumstances, The Problem solving Session organized for the course An Introduction to Artificial Intelligence is cancelled. -NPTEL Team

An Introduction to Artificial Intelligence - Problem Solving Session Recording is available!!

Dear Learner, We have uploaded the Recorded videos of the Live Interaction Session - Problem solving Session of Week 1 . Videos are uploaded inside the Separate Unit called " Problem solving Session " along with the slides used wherever applicable. Login to the course on swayam.gov.in to check the same. -NPTEL Team

An Introduction to Artificial Intelligence : Week 4 content is live now

Dear Students, The lecture videos for Week 4 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=44&lesson=45 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment-4 for Week-4 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=44&assessment=173 The assignment has to be submitted on or before Wednesday,[22/02/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

An Introduction to Artificial Intelligence : Problem solving Session

Dear learners, There will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Date: February 6, 2023 - Monday Time:06.00 PM - 08.00 PM Link to join: https://teams.microsoft.com/dl/launcher/launcher.html?url=%2F_%23%2Fl%2Fmeetup-join%2F19%3Ameeting_YmZlM2FjYmMtM2JlOS00NTJjLWE1ZjUtYTc0NDcwNTI4NzAx%40thread.v2%2F0%3Fcontext%3D%257b%2522Tid%2522%253a%25226f15cd97-f6a7-41e3-b2c5-ad4193976476%2522%252c%2522Oid%2522%253a%2522afec2e0e-e097-4995-a01b-9de849233f59%2522%257d%26anon%3Dtrue&type=meetup-join&deeplinkId=2cf54aea-c9f7-41d3-9899-446d9ef3bfdc&directDl=true&msLaunch=true&enableMobilePage=true&suppressPrompt=true Happy Learning. -NPTEL Team

An Introduction to Artificial Intelligence : Week 3 content is live now

Dear Students, The lecture videos for Week 3 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=35&lesson=36 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment-3 for Week-3 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=35&assessment=170 The assignment has to be submitted on or before Wednesday,[15/02/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

Dear learner, Every week there will be a live interactive session where a Course team member will explain some sample problems, how they are solved - that will help you solve the weekly assignments. We invite you to join the session and get your doubts cleared and learn better. Session 1 : Start Date: February 3, 2023 When: Every Friday Time: 07.00 PM - 09.00 PM Link to join: https://meet.google.com/hny-bzpv-ckp Session 2 : Start Date: February 3, 2023 When: Every Friday Time: 06.00 PM - 08.00 PM Link to join: https://meet.google.com/hhp-gmge-bes Session 3 : Start Date: February 4, 2023 When: Every Saturday Time: 07.00 PM - 09.00 PM Link to join: https://meet.google.com/nqn-pvkr-ega Session 4 : Start Date: February 6, 2023 When: Every Monday Time: 06.00 PM - 08.00 PM Link to join: https://teams.microsoft.com/dl/launcher/launcher.html?url=%2F_%23%2Fl%2Fmeetup-join%2F19%3Ameeting_YmZlM2FjYmMtM2JlOS00NTJjLWE1ZjUtYTc0NDcwNTI4NzAx%40thread.v2%2F0%3Fcontext%3D%257b%2522Tid%2522%253a%25226f15cd97-f6a7-41e3-b2c5-ad4193976476%2522%252c%2522Oid%2522%253a%2522afec2e0e-e097-4995-a01b-9de849233f59%2522%257d%26anon%3Dtrue&type=meetup-join&deeplinkId=2cf54aea-c9f7-41d3-9899-446d9ef3bfdc&directDl=true&msLaunch=true&enableMobilePage=true&suppressPrompt=true Thank you. -NPTEL team

An Introduction to Artificial Intelligence : Week 2 content is live now

Dear Students, The lecture videos for Week 2 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=28&lesson=29 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment-2 for Week-2 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=28&assessment=167 The assignment has to be submitted on or before Wednesday,[08/02/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

An Introduction to Artificial Intelligence : Week 1 Assignment is live now

Dear Students, Assignment-1 for Week-1 is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=17&assessment=166 The assignment has to be submitted on or before Wednesday,[08/02/2023], 23:59 IST . As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards,

An Introduction to Artificial Intelligence : Week 1 Video content is live now

Dear Students, The lecture videos for Week 1 have been uploaded for the course " An Introduction to Artificial Intelligence" . The lectures can be accessed using the following link: Link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=17&lesson=18 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). As we have done so far, please use the discussion forums if you have any questions on this module. Thanks and Regards,

An Introduction to Artificial Intelligence : Assignment 0 is live now

Dear Learners, We welcome you all to this course " An Introduction to Artificial Intelligence" . The assignment 0 has been released. This assignment is based on a prerequisite of the course. You can find the assignment in the link: https://onlinecourses.nptel.ac.in/noc23_cs05/unit?unit=16&assessment=165 Please note that this assignment is for practice and it will not be graded. Thanks & Regards

NPTEL: Exam Registration is open now for Jan 2023 courses!

Dear Learner,

Here is the much-awaited announcement on registering for the Jan 2023 NPTEL course certification exam.

1. The registration for the certification exam is open only to those learners who have enrolled in the course.

2. If you want to register for the exam for this course, login here using the same email id which you had used to enroll to the course in Swayam portal. Please note that Assignments submitted through the exam registered email id ALONE will be taken into consideration towards final consolidated score & certification.

3 . Date of exam: Apr 29, 2023

CLICK HERE to register for the exam.

Choose from the Cities where exam will be conducted: Exam Cities

4. Exam fees:

If you register for the exam and pay before Mar 17, 2023, 5:00 PM, Exam fees will be Rs. 1000/- per exam .

5. 50% fee waiver for the following categories:

6. Last date for exam registration: Mar 17, 2023, 5:00 PM (Friday).

7. Mode of payment: Online payment - debit card/credit card/net banking/UPI.

8. HALL TICKET:

The hall ticket will be available for download tentatively by 2 weeks prior to the exam date . We will confirm the same through an announcement once it is published.

9. FOR CANDIDATES WHO WOULD LIKE TO WRITE MORE THAN 1 COURSE EXAM:- you can add or delete courses and pay separately – till the date when the exam form closes. Same day of exam – you can write exams for 2 courses in the 2 sessions. Same exam center will be allocated for both the sessions.

10. Data changes:

Last date for data changes: Mar 17, 2023, 5:00 PM :

All the fields in the Exam form except for the following ones can be changed until the form closes.

The following 6 fields can be changed ONLY when there are NO courses in the course cart. And you will be able to edit the following fields only if you: -

6. What is your role ?

But, for cancelled courses, refund of fees will be initiated only after 2 weeks.

11. LAST DATE FOR CANCELLING EXAMS and getting a refund: Mar 17, 2023, 5:00 PM

12. Click here to view Timeline and Guideline : Guideline

An Introduction to Artificial Intelligence: Welcome to NPTEL Online Course - Jan 2023!!

Every week, about 2.5 to 4 hours of videos containing content by the Course instructor will be released along with an assignment based on this. Please watch the lectures, follow the course regularly and submit all assessments and assignments before the due date. Your regular participation is vital for learning and doing well in the course. This will be done week on week through the duration of the course.
Please do the assignments yourself and even if you take help, kindly try to learn from it. These assignments will help you prepare for the final exams. Plagiarism and violating the Honor Code will be taken very seriously if detected during the submission of assignments.
The announcement group - will only have messages from course instructors and teaching assistants - regarding the lessons, assignments, exam registration, hall tickets, etc.
The discussion forum (Ask a question tab on the portal) - is for everyone to ask questions and interact. Anyone who knows the answers can reply to anyone's post and the course instructor/TA will also respond to your queries.
Please make maximum use of this feature as this will help you learn much better.
If you have any questions regarding the exam, registration, hall tickets, results, queries related to the technical content in the lectures, any doubts in the assignments, etc can be posted in the forum section
The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: April 29, 2023 Morning session 9am to 12 noon; Afternoon Session 2 pm to 5 pm.
Registration URL: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.
Once again, thanks for your interest in our online courses and certification. Happy learning.

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Assignments

You can download the assignments here (in PDF format). Also check out assignment’s pages for any additional info.

Assignment #1 - Search problems
Assignment #2 - Minimax
Assignment #3 - MDP
Assignment #4 - RL

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NPTEL An Introduction to Artificial Intelligence Assignment 1 Answers 2022 | Week 1 Answers: QUIZXP

by QuizXp Team
January 22, 2022 February 22, 2022

Are you looking for the Answers to NPTEL An Introduction to Artificial Intelligence Assignment 1 – IIT Delhi? This article will help you with the answer to the Nation al Programme on Technology Enhanced Learning ( NPTEL ) Course “ NPTEL An Introduction to Artificial Intelligence Assignment 1 “

What is NPTEL An Introduction to Artificial Intelligence?

An Introduction to Artificial Intelligence by IIT Delhi course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem. It also teaches many first algorithms to solve each formulation. The course prepares a student to take a variety of focused, advanced courses in various subfields of AI.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the average of best 8 assignments out of the total 12 assignments given in the course. Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Below you can find the answers for NPTEL An Introduction to Artificial Intelligence Assignment 1

NPTEL An Introduction to Artificial Intelligence Assignment 1 Answers:-

Q1. Who is known as the “Father of Artificial Intelligence”?

Answer:- c – John McCarthy

Q2. What were the reasons leading to the first AI winter in 1974-80 ?

Answer:- a,c,d

Q3. Which of the following programs completed a major mathematical proof?

Q4. Which of the following is/are the reason(s) for the recent takeoff of deep learning?

Answer:- b,c,d

???? Next Week Answers: Assignment 02 ????

Q5. Strong AI hypothesis says that

Q6. Which action is a rational agent expected to prioritize?

Q7. Consider an AI agent which is given the task of sorting an array A of n distinct integers where each element ∈ {1,2,..n}. In each time step, the agent is allowed to swap two consecutive elements in the array. What are the state representations which provide complete information about the array?

Answer:- We are a little confused about the answers of 7 and 9 will update soon and notify you on the telegram channel if you know the answer then please comment down below .

Note:- We are going to post answers for all weeks you can join us on telegram for regular updates and if there are any changes in answers then will update on our telegram channel only.

Q8. Which game was AlphaGo built for, and which company was responsible for its development?

Q9. Why is Mathematical Logic considered to be an important part of AI?

Q10. What is an expert system in the context of AI?

For other courses answers:- Visit

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Disclaimer: We do not claim 100% surety of answers, these answers are based on our sole knowledge, and by posting these answers we are just trying to help students, so we urge do your assignment on your own.

if you have any suggestions then comment below or contact us at [email protected]

If you found this article Interesting and helpful, don’t forget to share it with your friends to get this information.

This is an older version of the course. See cs50.harvard.edu/ai for the latest!

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

How to Take this Course

Even if you are not a student at Harvard, you are welcome to “take” this course for free via this OpenCourseWare by working your way through the course’s seven weeks of material. If you’d like to submit the course’s seven projects for feedback, be sure to create an edX account , if you haven’t already. Ask questions along the way via any of the course’s communities !

If interested in a verified certificate from edX , enroll at cs50.edx.org/ai instead.
If interested in a professional certificate from edX , enroll at cs50.edx.org/programs/ai instead.
If interested in transfer credit and accreditation from Harvard Extension School , register at web.dce.harvard.edu/extension/csci/e/80 instead.
If interested in transfer credit and accreditation from Harvard Summer School , register at web.dce.harvard.edu/summer/csci/s/80 instead.

How to Teach this Course

If you are a teacher, you are welcome to adopt or adapt these materials for your own course, per the license .

IMAGES

Introduction of Artificial Intelligence by Learning and Teaching Online
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COMMENTS

PDF AN INTRODUCTION TO ARTIFICIAL INTELLIGENCE
2 • Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals, such as "learning" and "problem solving. . In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and
Introduction to Artificial Intelligence (AI) Course by IBM
There are 4 modules in this course. In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get ...
CS50's Introduction to Artificial Intelligence with Python
By course's end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own. Prerequisites. CS50x or at least one year of experience with Python. Watch an introduction.
An Introduction to Artificial Intelligence
An Introduction to Artificial Intelligence. The course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem.
NPTEL :: Computer Science and Engineering
Sl.No Chapter Name English; 1: 1. Artificial Intelligence: Introduction: Download Verified; 2: 2. Introduction to AI: Download Verified; 3: 3. AI Introduction: Philosophy
CS 188: Introduction to Artificial Intelligence, Spring 2021
Description. This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially ...
NOC
The course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem. It also teaches many first algorithms to solve each ...
CS47100: Introduction to Artificial Intelligence (Spring 2023)
Course Information. Artificial intelligence (AI) is about building intelligent machines that can perceive and act rationally to achieve their goals. To prepare students for this endeavor, we cover the following topics in this course: Search, constraint satisfaction, logic, reasoning under uncertainty, machine learning, and planning.
CS486: Introduction to Artificial Intelligence
For CS486 students: Assignments (4) (40% - to be done individually - dates to be announced - see below for tentative dates). One and a half hour written midterm examination (15% - Jun 8, 2022, 700pm-850pm in M3-1006). Two and a half hour written final examination (Aug 8, 2022 730pm-1000pm in M3-1006) (45% and must pass the final to pass the ...
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Russell and Norvig. Artificial Intelligence: A Modern Approach. A comprehensive reference for all the AI topics that we will cover. Koller and Friedman. Probabilistic Graphical Models. Covers factor graphs and Bayesian networks (this is the textbook for CS228). Sutton and Barto. Reinforcement Learning: An Introduction.
CS221: Artificial Intelligence: Principles and Techniques
The use of generative AI tools to substantially complete an assignment or exam (e.g. by directly copying) is prohibited and will result in honor code violations. ... The goal of artificial intelligence (AI) is to tackle complex real-world problems with rigorous mathematical tools. ... Introduction (Sanmi) 10:30am - 12:00pm: Lecture: Machine ...
NPTEL :: Computer Science and Engineering
NPTEL :: Computer Science and Engineering - NOC:An Introduction to Artificial Intelligence. Courses. Computer Science and Engineering. NOC:An Introduction to Artificial Intelligence (Video) Syllabus. Co-ordinated by : IIT Delhi. Available from : 2019-11-13. Lec : 1.
Introduction to Artificial Intelligence
Assignment 2: Group 20 - 30% 2,3 Final-Semester Test: Individual 20 - 30% 1,2 Mid-Semester Test: Individual 20 - 30% 1,2 Content. Introduction to Artificial Intelligence and Intelligent Agents; Introduction to Logic and Reasoning; Uninformed and Informed Search; Knowledge Representation; Expert Systems;
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Re-evaluation for the course "An Introduction to Artificial Intelligence" Dear Student; Re-evaluation has been done by changing the answer for Question 7 in Assignment 10. Students are requested to find their revised scores of Assignment 10 in the Progress page. ... An Introduction to Artificial Intelligence : Week 1 Assignment is live now
Introduction to Artificial Intelligence: Assignments
Also check out assignment's pages for any additional info. Assignment #1 - Search problem... Iran University of Science and Technology Introduction to Artificial Intelligence Spring 1398-1399
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This video shows the answers for week 9 assignment 9 of nptel course An Introduction to Artificial Intelligence
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From Artificial Intelligence to Brain Intelligence. Research in Artificial Intelligence (AI) is not new, it has been around since 1950's. AI resurfaced at that time while Moore's law was on an aggressive path of scaling, with the transformation of NMOS and later bipolar technology to CMOS for high performance, low power as well as low cost applications.
NPTEL Introduction to Artificial Intelligence (AI) Assignment 6
NPTEL Introduction to Artificial Intelligence (AI) Assignment 6 Solution #ai #nptel #ntptelsolution #nptelcourseanswers #assignmentsolutions #patentlawassign...
NPTEL An Introduction to Artificial Intelligence Assignment 1 ...
An Introduction to Artificial Intelligence by IIT Delhi course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem.
CS50's Introduction to Artificial Intelligence with Python
By course's end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own. Prerequisites. CS50x or at least one year of experience with Python. Watch an introduction.
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Through practical assignments, you can apply the knowledge gained in real-world scenarios. This experiential learning approach helps reinforce your understanding and prepares you for the challenges of the AI landscape. ... Stanford University offers the "Introduction to Artificial Intelligence" course. Led by the prominent AI expert Sebastian ...
An Introduction to Artificial Intelligence
#nptel #nptelanswers #nptelassignmentanswers #swayam #nptel2024Join this channel to get access to perks:https://www.youtube.com/channel/UCwAhi3lWNCLT47sLkhU1...
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Introduction to the program: ... Artificial intelligence and machine learning ... Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate ...