CSL 302 - Artificial Intelligence - Spring 2018


Course Information Grading Policy Lectures/Calendar Labs


Course Information


Timings and Lecture Hall

Monday 3.50-4.40pm

Tuesday 4.45-5.35pm

Wednesday 2.00-2.50pm

Transit Campus II (NIELIT Campus) - H8



Artificial Intelligence (AI) in an important area of Computer Science. AI is a well studied subject with utility in many real-world applications. This introductory course discusses some of the basic and widely used AI techniques, covering a wide range of topics such as search, AI for games, logic, planning, and reasoning. For a comprehensive list of topics covered in the course and course schedule, please see the course calendar. Practical experience will be gained through implementing the AI algorithms for different applications in C/C++. For more details on lab assignments, please see the Labs webpage. This course has a pre-requisite of CSL201 (Data Structures)


Reference Material

Primary textbook - Stuart Russell and Peter Norvig, Artificial Intelligence - A Modern Approach, Third Edition, Prentice Hall 2009

Other reference books

  • Artificial Intelligence by Rich and Knight


Instructor Details

Narayanan (CK) Chatapuram Krishnan

Office Hours: Monday 4.00-5.00pm and Friday 3.30-4.00pm

Office: 318

Phone: +91 1881 242273

Email: ckn@iitrpr.ac.in


Teaching Assistants Details

Akanksha Paul

Email: akanksha.paul@iitrpr.ac.in

Shreya Ghosh

Email: shreya.ghosh@iitrpr.ac.in

Jaspinder Kaur

Email: 2017csz0002@iitrpr.ac.in


Academic Integrity

It is expected that students who are taking this course will demonstrate a keen interest in learning and not mere fulfilling the requirement towards their degree. Discussions that help the student understand a concept or a problem is encouraged. However, each student must turn in original work. Plagiarism/copying of any form, will be dealt with strict disciplinary action. This involves, copying from the internet, textbooks and any other material for which you do not own the copyright. Copying part of the code will be considered plagiarism. Lending the code to others will be considered plagiarism too, for it is difficult to investigate who copied whose code. Students who violate this policy will directly receive a failing grade in the course. Remember - Your partial submission can fetch you some points, but submitting other's work as your own can result in you failing the course. Please talk to the instructor if you have questions about this policy. All academic integrity issues will be handled in accordance with institute regulations.


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Grading Policy


Grading Policy

Quizzes: There will be 6 pre-announced quizzes during the semester. Check the course calendar to learn about dates on which a quiz will be held. The top 5 quiz scores will count towards the student's overall grade. The quizzes will account for 20% of the overall grade. The quizzes will be held during the first hour of the lab.


Labs: There will be approximately 5 labs. Each lab will have a major programming component and will span for approximately two-three weeks. The top 4 labs will account for 30% of the overall grade. Students having difficulty with the labs are encouraged to contact the TA for assistance. You are not required to be physically present in the lab during the lab hours. You can complete the labs at your convenience and turn it in by the deadline. There will be penalty for late submission of the labs. It will start at 1% for the first hour after the submission deadline and increase exponentially for every hour hence forth.


Exams: The mid and end semester exams together will account for 50% (25% each) of the overall grade.


Attendance: There is no mandatory attendance. However attendance will be taken in every class. This will consitute a bonus of 1% for the final grade and might be helpful for all border line students.


Passing Critera: A student must secure an overall score of 40 (out of 100) and a combined score of 60 (out of 200) in the exams to pass the course.


Tentative Grade Breakup*

Quizzes (5 out of 6) 20%
Labs (4 out of 5) 30%
Mid-Semester Exam 25%
End-Semester Exam 25%
Total 100

*This is a tentative breakup of the grades and can change at the discretion of the instructor. However, any change with respect to the grade break-up will be intimated in advance.


Grade Sheet:PDF

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Lectures and Calendar


Tentative Schedule and List of Topics*

Topic Readings
Introduction and Intelligent Agents

Chapters 1 and 2

Uninformed Search

Chapter 3(3.1-3.4)

Informed Search

Chapter 3(3.5-3.6)

Q1 (Jan 25)
Local Search

Chapter 4(4.1-4.5)

L1 (Feb 1)
Adversarial Search

Chapter 5

Q2 (Feb 8)
Constraint Satisfaction Problems

Chapter 6

Q3 (Feb 15)
Logical Agents

Chapter 7

L2 (Feb 21)
Exam week

mid-sem, solution

Propositional Logic

Chapter 7


Chapter 10,

Chapter 13 (Rich and Knight)

Probabilistic Reasoning I

Chapter 13 and 14

L3 (Mar 22)
Probabilistic Reasoning II

Chapter 13 and 14

Markov Decision Process I

Chapter 17

Q4 (Apr 5)
Markov Decision Process II

Chapter 17

Q5 (Apr 12)
Reinforcement Learning I

Chapter 21

L4 (Apr 19)
Reinforcement Learning II

Chapter 21

Q6 (Apr 26)
Reserve Week


L5 (May 1 5)
Exam week

end-sem, solution

*This is a tentative list of topics that will be covered during the semester. The topics and schedule can change according to the need at the discretion of the instructor.

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