Schedule
- Mon, Jan 12 Intelligent Agents [Chapter 1)
- Wed, Jan 14 Uninformed Search [Sections 10.2.1 and Sections 3.1-3.4]
- Mon, Jan 19 No Class - Martin Luther King’s Birthday
- Wed, Jan 21 Informed Search [Sections 3.5-3.6] (handout)
- Mon, Jan 26 Logic and Reasoning (1) [Sections 7.1-7.5] [Satish Kumar substitutes since Sven is at AAAI-15]
- Wed, Jan 28 Logic and Reasoning (2) [Chapter 8, possibly Section 9.2 and 9.5] (handout) Satish Kumar substitutes since Sven is at AAAI-15
- Fri, Jan 30 Drop Date 1
- Mon, Feb 02 Knowledge Representation (1) [Sections 9.3, 9.4]
- Wed, Feb 04 Knowledge Representation (2) [Sections 12.5-12.6]
- Mon, Feb 09 Intelligent Agents and Problem Solving [Chapter 2]
- Wed, Feb 11 STRIPS [Section 10.1] (handout - problem courtesy of Russell and Norvig)
- Mon, Feb 16 No Class - Presidents’ Day
- Wed, Feb 18 Search-Based Planning [Section 10.2.3]
- Mon, Feb 23 Local and Online Search [Sections 4.1-4.2, possibly Section 4.5]
- Wed, Feb 25 Midterm 1
- Mon, Mar 02 Constraint Satisfaction (Courtesy of Russell) [Chapter 6]: T.K. Satish Kumar lectures
- Wed, Mar 04 SAT-Based Planning [Chapter 10.4.1]
- Mon, Mar 09 Adversarial Search [Sections 5.1-5.3]
- Wed, Mar 11 Adversarial Search [Sections 5.1-5.3]
- Mon, Mar 16 No Class - Spring Break
- Wed, Mar 18 No Class - Spring Break
- Mon, Mar 23 Probabilistic Reasoning (1) [Chapter 13]
- Wed, Mar 25 Probabilistic Reasoning (2) [Sections 14.1-14.4]
- Mon, Mar 30 Probabilistic Reasoning (2) [Sections 14.1-14.4]
- Wed, Apr 01 Maximum Likelihood and Naive Bayes' Learning (Courtesy of Moore) [Sections 20.1 and 20.2.1-20.2.2] (take your own notes)
- Mon, Apr 06 EM Algorithm (Courtesy of Choudhury) [Section 20.3] and Decision Theory [Sections 16.1 and 16.5]
- Wed, Apr 08 Midterm 2
- Fri, Apr 10 Drop Date 2
- Mon, Apr 13 Markov Decision Processes [Sections 17.1-17.2]
- Wed, Apr 15 Decision Tree Learning [Section 18.3]
- Mon, Apr 20 Perceptrons and Neural Networks [Section 18.7.1-18.7.4]
- Wed, Apr 22 Perceptron Learning (handout and program)
- Mon, Apr 27 Neural Network Learning [Section 18.7.1-18.7.4] (handout 1 and handout 2) [There are two typos in handout 1: The derivative of the sigmoid function should be g'(x) = e^(-x)/(1+e^(-x))^2 and the page reference should be to pages 733ff.] and Nearest Neighbor Learning (Sections 18.8.1)
- Wed, Apr 29 Wrap Up [Chapters 26-27]
- Fri, May 08 Final (2:00pm-4:00pm) in our regular class room (unless announced otherwise)
For the schedule of the projects, please see the "Projects" menu item on the left.