Announcements

Syllabus

Class policies

Schedule

Resources

Course Schedule

B551 Fall 2011

(subject to change)

 

No.

Date

 

Subject

Out

In

Readings

1

30-Aug

Tu

Class overview. Intro to AI

 

 

R&N 1,26

2

1-Sep

Th

Search

HW1

 

R&N 3.1-3

3

6-Sep

Tu

Search, pt 2

 

 

R&N 3.4

4

8-Sep

Th

Heuristic search

 

 

R&N 3.5

5

13-Sep

Tu

Beyond classical search

 

 

R&N 4.1-5, 6.1-3

6

15-Sep

Th

Game playing

HW2

HW1

R&N 5.1-4

7

20-Sep

Tu

Partially observable, stochastic games

 

 

R&N 5.5-6

8

22-Sep

Th

Introduction to uncertainty

 

 

R&N 4.3-4,13.1-2

9

27-Sep

Tu

Probabilistic inference

 

 

R&N 13.3-6

10

29-Sep

Th

Bayesian networks

HW3

HW2

R&N 14.1-3

11

4-Oct

Tu

Bayesian networks, pt 2

 

 

R&N 14.4-5

12

6-Oct

Th

Probabilistic temporal models

HW4

HW3

R&N 15

13

11-Oct

Tu

Probabilistic temporal models, pt 2

 

 

R&N 15

14

13-Oct

Th

Statistical learning

 

 

R&N 20.1-2

15

18-Oct

Tu

Intro to machine learning

 

 

R&N 18.1-2

16

20-Oct

Th

Decision tree learning

HW5

HW4

R&N 18.3

17

25-Oct

Tu

Neural networks

 

 

R&N 18.6-7

18

27-Oct

Th

Support vector machines

HW6

HW5

R&N 18.8-9

19

1-Oct

Tu

Evaluating learning

 

 

R&N 18.4-5

20

3-Nov

Th

Intelligent agents

 

 

R&N 2

21

8-Nov

Tu

Decision theoretic planning

 

 

R&N 17.1-4

22

10-Nov

Th

Partially observable problems

HW7

HW6

R&N 11.1-4

23

15-Nov

Tu

Reinforcement learning

 

 

R&N 21.1-2

24

17-Nov

Th

Computer vision
(Guest lecture, David Crandall)

HW8

HW7

 

25

22-Nov

Tu

Partially observable problems

 

 

 

---

24-Nov

Th

 

 

 

 

26

29-Nov

Tu

Robot motion planning

 

 

 

27

1-Dec

Th

Natural language processing

(Guest lecture, Mike Gasser)

 

 

 

28

6-Dec

Tu

Review

 

 

 

29

8-Dec

Th

Final project presentations

Practice solns

HW8

 

Homework assignments

HW1. Uninformed search and heuristic search.  (partly written, partly programming)

HW2. Minimax search for the Gobblet game. (programming)

HW3. Probabilistic inference and Bayesian networks. (written)

HW4. Bayesian networks for a voter prediction application. (programming)

HW5. Decision trees and statistical learning.  (written)

HW6. Machine learning for text classification. (programming)

HW7. Intelligent agents and decision theoretic planning. (written)

HW8. Integrated perception and planning for multiagent interaction. (programming)