Artificial Intelligence Prelim Syllabus
As undergraduate material, students are expected to be familiar with S.
Russell and P. Norvig's Artificial
Intelligence: A Modern Approach (second edition).
Graduate material that students are expected to be familiar with is
- Learning and Probabilistic Inference
- Natural Language and Speech.
(1) Learning and Probabilistic Inference
- R. Sutton and A. Barto, Reinforcement
Learning, Chapters 1-6, 8, 11
- Shawe-Taylor, J. & Cristianini, N.
Kernel Methods for Pattern Analysis
Cambridge University Press, 2004.
Chapters 2, 3, 5, 6 and 7
A selection of chapters from Jordan are available from
Chap 2: Conditional Independence and Factorization
Chap 3: The Elimination Algorithm
Chap 4: Probability Propagation and Factor Graphs
Chap 5: Statistical Concepts
Chap 6: Linear Regression and the LMS Algorithm
Chap 7: Linear Classification
Chap 10: Mixtures and Conditional Mixtures
Chap 11: The EM Algorithm
Chap 12: Hidden Markov Models
Chap 17: The Junction Tree Algorithm
- D. Forsyth and J. Ponce, Computer Vision --A Modern Approach, Chapters 1, 4, 7, 8, 9, 14, 18, 22.
(3) Natural Language and Speech