CS 337 and CS 335:
Artificial Intelligence and Machine Learning
(Spring 2019)

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This page serves as the primary resource for CS 337 (Artificial Intelligence and Machine Learning) and CS 335 (Artificial Intelligence and Machine Learning Lab).


  Shivaram Kalyanakrishnan
  Office: Room 220, New CSE Building
  Phone: 7704
  E-mail: shivaram@cse.iitb.ac.in

Teaching Assistants

  Vishwajeet Singh (E-mail: vsbagdawat@cse.iitb.ac.in).
  Huzefa Chasmai (E-mail: chasmai@cse.iitb.ac.in).
  Rishabh Shah (E-mail: rishshah@cse.iitb.ac.in).
  Abhinav Goyal (E-mail: abhigoyal@cse.iitb.ac.in).
  Manas Bhargava (E-mail: manas@cse.iitb.ac.in).
  Ashwini Pale (E-mail: ashwini@cse.iitb.ac.in).
  Rakesh Khobragade (E-mail: rkhobrag@cse.iitb.ac.in).
  Dhananjay Kumar Sharma (E-mail: dhananjayk@cse.iitb.ac.in).

Class Meetings

Lectures will be held in LA 201 in Slot 6: 11.05 a.m. – 12.30 p.m. Wednesdays and Fridays. Lab sessions will be held in Software Lab 2, New CSE Building, during Slot L4: 2.00 p.m. – 4.55 p.m. Fridays. A portion of the lab sessions will be used for lectures and assessments; the schedule will be announced in class beforehand. If at all a lecture or test is scheduled during the lab session, it will be held 2.00 p.m. – 3.25 p.m. in LH 301.

The instructor's office hours will immediately follow class lectures and labs. Meetings can also be arranged by appointment.

Course Description

Artificial Intelligence (AI) surrounds us today: in phones that respond to voice commands, programs that beat humans at Chess and Go, robots that assist surgeries, vehicles that drive in urban traffic, and systems that recommend products to customers on e-commerce platforms. This course aims to familiarise students with the breadth of modern AI, to impart an understanding of the dramatic surge of AI in the last decade, and to foster an appreciation for the distinctive role that AI can play in shaping the future of our society. The resurgence of AI has been facilitated in large part by the field of machine learning (ML), whose essential elements will be introduced as a part of this course.

The course will provide a historical perspective of the field of AI and discuss of its foundations in search, knowledge representation and reasoning, and machine learning. A small selection of specialised topics will also be taken up; these could include, for example, speech and natural language processing, robotics, crowdsourcing, computer vision, and multiagent systems. The theory and lab components will proceed in step to equip students with the knowledge and skills to design and apply solutions based on AI and ML.

Students interested in gaining more depth are encouraged to follow this basic course with advanced ones on topics such as machine learning, information retrieval and data mining, sequential decision making, robotics, speech and natural language processing, computer vision, and game theory.


CS 337 and CS 335 are core courses in the CSE undergraduate programme. They can only be taken by CSE B.Tech. students in their third (or higher) year. Other students are welcome to sit through lectures in CS 337, but may not formally register (whether for credit or for audit) for either course.


CS 337 will have four class tests (each 20 marks) and an end-semester examination (40 marks). The best three scores from the class tests will contribute 60 marks towards the final grade; the end-semester examination will contribute 40 marks towards the final grade.

Grades for CS 335 will be decided based on 8–10 lab assignments, each worth 10–15 marks.

Academic Honesty

Students are expected to adhere to the highest standards of integrity and academic honesty. Acts such as copying in the examinations and sharing code/viewing on-line solutions for the lab assignments will be dealt with strictly, in accordance with the institute's procedures and disciplinary actions for academic malpractice.

Texts and References

Artificial Intelligence: A Modern Approach, Stuart J. Russell and Peter Norvig, 3rd edition, Pearson, 2010.

The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2nd edition, Springer, 2009.


This page will serve as the primary source of information regarding CS 337 and CS 335, their schedules, and related announcements. The Moodle pages for these courses will be used for sharing additional resources for the lectures and assignments, and also for recording grades.

E-mail is the best means of communicating with the instructor; students must send e-mail with "[CS337]" in the header, with a copy marked to the TAs.

Class Schedule

Lab Assignments and Schedule

Lab Assignments will be published on Tuesdays, and will be due for submission by 11.55 p.m. the following Monday. It is expected that students will attend the Friday lab sessions after reading the published statement and making at least partial progress towards the solution. The instructor and TAs will provide guidance as required during the lab session.

Submissions must be uploaded to Moodle in the format specified. If a submission is not made by the associated deadline, a "carry over" will be counted against the assignment. Assignments that are carried over will only be evaluated after the student attends a session with a TA or the instructor to explain their submission and demonstrate its working. A special lab session will be announced to evaluate carry-over assignments.

A student may carry over up to two lab assignments without any penalty. A third carry-over will incur a penalty of 2 marks; a fourth carry-over will incur a penalty of 4 marks; subsequent carry-overs will incur a penalty of 6 marks.

Below is the schedule for lab assignments.