Laboratory of Statistical Artificial Intelligence and Machine Learning

Artificial Intelligence DS2020 Spring 2024

Description

Artificial intelligence (AI) is an important discpline with numerous real-world applications. This introductory course covers a variety of basic and widely used AI techniques, including search, AI for games, logic, planning, and reasoning. A complete list of topics covered in the course can be found in the course schedule.

Lecture and Lab Timings

Monday 11.00-11.50am
Wednesday 11.00-11.50am
Friday 11.00-11.50am

Saturday 10.00am-12.00pm - Compensatory classes

Lab Wednesday 2.00-3.30pm.

Reference Material

Primary Textbook : Artificial Intelligence - A Modern Approach, 3rd Edition, Stuart Russell and Peter Norvig, Prentice Hall
Other Reference Books: Artificial Intelligence By Rich, Knight and Nair

Academic integrity

Students enrolled in this course are expected to exhibit a strong desire to learn, rather than just fulfilling a requirement for their degree. Engaging in discussions that help students better understand concepts or problems is encouraged. However, all submitted work must be original. Plagiarism, including copying from the internet, textbooks, or any other source for which the student does not hold the copyright, as well as sharing code with other students, will not be tolerated and will result in strict disciplinary action, including a failing grade in the course. If you have any questions about this policy, please contact the instructor. All academic integrity violations will be handled in accordance with institute regulations.

Grading Policy

Tests Two tests will be conducted during the semester. Check the course calendar for the quiz dates. Each quiz will account for 15% of your overall grade.

Labs There will be 6 programming labs during the semester that will account for 30% of the overall grade.

Exams There will be an end-semester exam that will account for 40% of the overall grade.

Attendance

This course follows the attendance criteria mandated by the institute.

Course Schedule

week 1 (2) - Introduction and Intelligent Agents (Compensatory class on Jan 20th, Saturday)
week 2 (2) - Problem Solving Agents
week 3 (1) - Search I, lab 1
week 4 (3) - Search II, (Compensatory class on Feb 3, Saturday)
week 5 (3 )- Adversarial Search, lab 2
week 6 (2) - Logical Agents and Propositional Logic
week 7 (3) - First Order Logic, lab 3
week 8 (3) - Planning (Compensatory class on March 2nd, Saturday)
week 9 (1) - Probabilistic Reasoning I lab 4
week 10 (3) - Probabilistic Reasoning II
week 11 (1) - Decision Theory, lab 5
week 12 (2) - Markov Decision Process I (Compensatory class on March 30, Saturday)
week 13 (3) - Markov Decision Process II lab 6
week 14 (3) - Reinforcement Learning I, (Compensatory class on April 20th, Saturday)
week 15 (3) - Reinforcement Learning II
week 16 (1) - Summary

Lecture Material

Students enrolled in the course can access the lecture material from Moodle