CS 330: Artificial Intelligence (2022)

In this course students will learn various techniques and applications of AI. Students are expected to have done at least one course on Probability-Statistics and Data Structures and Algorithms. There will be a lab component for this course which will involve a good deal of programming (primarily in python).

Instructor: Dr. Divya Padmanabhan Teaching Assistants: Akanksha, Diptiman, Umang

Faculty Intern: Ashweta

Lecture Timings: Tues 8-9 a.m Tues 3 - 4 pm (LT3), Wed 12 noon - 1 pm (LT3) , Fri 10 am - 11 a.m (LT3)

Lab Timings: Tues 4-6 pm (Computing Center)

Resources

The course will closely follow CS 188 Artificial Intelligence at Berkeley University and students are strongly encouraged to refer to the material from the CS 188 course page. Additionally some of CS188 lectures are available on youtube.

Textbook References:

• [RN] Stuart Russel and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Edition, Pearson.

• [SB] Richard Sutton and Andrew Barto, Introduction to Reinforcement Learning.

• [Bishop] Christopher Bishop, Pattern Recognition and Machine Learning.

• [Nielsen] Michael Nielsen, Neural Networks and Deep Learning

• Resources for pre-requisite topics:

  • 1. 1. Sheldon Ross, Introduction to Probability Models

       2. Bertsekas and Tsitsikilis, Introduction to Probability

       3. Cormen, Lieserson, Rivest and Stein, Introduction to Algorithms

Lab Resource: CS 188 Pacman Projects

Grading

Theory (80% weightage): The theory part comprises assignments (25%), a mid-term exam (30%), a final exam (30%) and class participation (15%). All assignments are compulsory. They are to be done individually. You are encouraged to discuss problem solving ideas for the assignments with your classmates. However the submitted assignment must be written/typed individually based on your own understanding.

Lab (20% weightage): The lab component will consist of several programming assignments. You may work in groups of two for all except programming assignment 0, which needs to be done individually.

Collaboration is strictly NOT permitted for the mid-term and final exams. Please approach me if you have any questions.

Lectures

In person.