Lecture notes will be linked to this web page, in PDF format. The reader for PDF files is available free from Adobe for UNIX, Apple Macintosh, and Windows.
|Introduction||RN, Chapter 1 & 2||Slides|
|Search (uninformed)||RN, Chapter 3||Slides|
|Search (informed)||RN, Chapter 3||Slides|
|Search (optimization)||RN, Section 4.1||Slides||Homework 1:
(new version with correction posted Jan.19 9:45am, see yellow highlight on p.2)
Sample code (save using extension .py instead of .txt)
|Constraint satisfaction||RN, Chapter 6||Slides|
|Search (under uncertainty)||RN, Sec. 4.2-4.5||Slides|
|Game playing (Minimax)||RN, Chapter 5||Slides||Homework 1 due.|
|Game playing (Monte-Carlo search trees)||RN, Chapter 5||Slides|
|Logical reasoning (propositional logic)||RN, Chapter 7||Slides||Homework 2:|
Minor modications to Q2 logical statements shown in yellow (Feb.5, 9:53)
These are optional, you can use the original formulation, though this may yield a different solution; we will accept both.
Fixed solve_sudoku() call in sudoku.py (Feb.3)
New error message for the human player (Feb.4, 15:14)
|Logical reasoning (first-order logic)||RN, Chapters 8, 9||Slides||Project code and instructions now available
(Version 2 posted Mar.11, deals with infinite moves).
|Sequential logical reasoning (classical planning)||RN, Chapter 10 ||Slides|
|Probabilistic reasoning (basics)||RN, Chapter 13||Slides||Homework 2 due next day (Feb.12).|
|Midterm Exam (CONFIRMED)
||Practice questions with some solutions. Old midterm.|
|Probabilistic reasoning (Bayesian networks)
||RN, Sec. 14.1, 14.2, 14.4||Slides|
|Probabilistic reasoning (Bayesian networks cont'd)||RN, Sec. 14.1, 14.2, 14.4||Slides||Homework 3:|
|Learning probabilistic models with complete data||RN, Sec. 20.1-20.2 (up to p.719) ||Slides|
|Learning probabilistic models with missing data||RN, Sec. 20.3||Slides||Homework 3 due.|
|Reasoning with temporal probabilistic models (HMMs)||RN, Sec. 15.1-15.3||Slides||Homework 4:|
Instructions (Ignore the last question.)
|Learning temporal probabilistic models (Baum-Welch)||RN, Sec. 20.3||Slides|
|Learning from examples||RN, Chapter 18||Slides|
|Reasoning with utilities||RN, Chapter 16||Slides||Homework 4 due.|
|Learning utilities from exploration (Bandits)||RN, Sec. 21.3||Slides|
|Sequential reasoning (Markov Decision Processes)||RN, Sec. 17.1 - 17.3||Slides||Final project code due.
Report due on Apr.1. See evaluation form.
|Sequential reasoning (Markov Decision Processes, cont'd)||Slides||Homework 5:|
|Learning sequential decisions (Reinforcement Learning)||RN, Chapter 21||Slides||Tutorial on problem solving with probabilistic models (Today: 4-6pm, MC103)|
|Reasoning under uncertainty (POMDPs) / AI present and future||RN, Sec. 17.4-5, Ch. 26, 27||Slides||Homework 5 due.|
|Final Exam (9am-12pm)|