COURSE LEARNING GOALSThe objective of the class is to:
(a) show how to identify the appropriate AI solutions for different classes of computational challenges and
(b) provide experience in implementing such solutions on representative challenges.
The course is intended for computer science graduate students, who have not been exposed to artificial intelligence material in the past. It can also appeal to students in related areas (such as psychology, mathematics, electrical, mechanical or biomedical engineering, etc.) who have interests in artificial intelligence methodologies and their applications.
Fridays 1:00-3:00 PM in CBIM 07
Chaitanya Mitash (email@example.com)
Aravind Sivaramakrishnan (firstname.lastname@example.org)
The class introduces fundamental ideas that have emerged over the past fifty years of AI research and provides a useful toolbox of AI algorithms. Example topics include:
(a) Deterministic Reasoning: Heuristic Search, Local Search, Adversarial Search, Constraint Satisfaction Problems
(b) Probabilistic Models: Bayesian Networks, Hidden Markov Models, Kalman and Particle Filters, (Partially Observable) Markov Decision Processes
(c) Machine Learning: Linear Models for Regression and Classification, Neural Networks, Kernel Methods, Gaussian Processes, Sparse Kernel Machines, Reinforcement Learning, Perception
Example textbooks include:
- "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig (Third Edition), Prentice Hall Series in Artificial Intelligence;
- "Pattern Recognition and Machine Learning" by Christopher Bishop, Springer
Regular readings and homeworks, some of which involve programming, and exams.
A midterm and a final examination. Typically the midterm exam covers the material on deterministic reasoning, as well bayesian networks and inference. The final exam also covers material on Markov Decision Processes and machine learning.
Final Exam: 20%
Final Project: 30%
TENTATIVE SCHEDULE (SUBJECT TO CHANGES)
Lecture 1 : Introduction and Overview [Slides in PDF]
Lecture 2 : Uninformed Search [Slides in PDF]
Lecture 3 : Heuristic Search [Slides in PDF]
Lecture 4 : Local Search [Slides in PDF]
Lecture 5 : Adversarial Search [Slides in PDF]
Lecture 6 : Constraint Satisfaction Problems [Slides in PDF]
Lecture 7 : Probabilistic Reasoning [Slides in PDF]
Lecture 8 : Bayesian Networks [Slides in PDF]
Lecture 9 : Markov Networks [Slides in PDF]
Lecture 10 : Temporal Models [Slides in PDF]
Lecture 11 : Kalman and Particle Filters [Slides in PDF]
Lecture 12 : Markov Decision Processes [Slides in PDF]