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

Tuesday 10.30-11.45am
Thursday 10.30-11.45am
Wednesday 2.00-3.30pm (most of the labs will be take home)

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 5 programming labs during the semester that will account for 25% of the overall grade. All labs are due on Thursday of the week.

Exams There will be an end-semester exam that will account for 45% of the overall grade.

Attendance

This course follows the attendance criteria mandated by the institute.

Course Schedule

week 1 (3) - Intelligent and Problem Solving Agents
week 2 (1.5) - Search I
week 3 (3) - Search II, lab 1
week 4 (3) - Adversarial Search
week 5 (3) - Logical Agents and Propositional Logic
week 6 (3) - First Order Logic,, lab 2
week 7 (3) - Buffer week, test 1,
week 8 (3) - Planning
week 9 (3) - Probabilistic Reasoning I, lab 3
week 10 (3) - Probabilistic Reasoning II
week 11 (3) - Decision Theory
week 12 (3) - Markov Decision Process I, test 2
week 13 (3) - Markov Decision Process II lab 4
week 14 (1.5) - Reinforcement Learning I
week 15 (3) - Reinforcement Learning II, lab 5
week 16 (1.5) - Summary

Lecture Material

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