CSCI 5512: Spring 2006 Course Outline
Instructor:
William Schuler (schuler at cs.umn.edu)
Office: 5-225F EE/CSci Building,
Ph: (612) 626-7502
Office Hours: Tu, Th 11:00 A.M. - 11:45 P.M., Th 5:15 P.M. - 5:45 P.M.
Teaching Assistants:
Kelly Cannon (cannon at cs.umn.edu)
Office Hours: Th 2:00 P.M. - 3:00 P.M. in EE/CSci 2-209
Steve Damer (damer at cs.umn.edu)
Office Hours: W 3:00 P.M. - 4:00 P.M. in EE/CSci 2-209
Textbook:
"Artificial Intelligence: A Modern Approach", Russell and Norvig. 2nd Edition, Prentice Hall, 2003. ISBN 0-13-790395-2.
Web site:
http://www-users.itlabs.umn.edu/classes/Spring-2006/csci5512/
Course Time and Location:
Lecture : Tu,Th 9:45 A.M. - 11:00 A.M. MechE 108
Course Content:
DISCLAIMER: Future lecture links may change as these are the slides from the previous year. Use these slides as a guide only.
| Wk |
Tuesday |
Thursday |
Friday |
Reading |
| 1 |
1/17
Probability Handout, Course Info, Uncertainty and Probability Concepts |
1/19
Probability and Independence |
1/20
HW0 Due |
Chap 13 |
| 2 |
1/24
Bayes Nets |
1/26
Exact Inference |
|
Chap 14 |
| 3 |
1/31
Message Passing |
2/2
Approximate Inference code output |
2/3
HW1 Due, HW1 Solution |
Chap 14/15 |
| 4 |
2/7
Temporal Models and HMMs
demo |
2/9
Dynamic Bayes Nets |
|
Chap 15 |
| 5 |
2/14
Graphical Models |
2/16
Speech Recognition |
2/17
Deadline for Teach for America applications
HW2, HW2 Solution
Due Mon (2/20) at Noon
|
Chap 15/16 |
| 6 |
2/21
Utility Theory
|
2/23
Decision Networks |
|
Chap 16/17 |
| 7 |
2/28
Sequential Decisions |
3/2
Multi-Agent Decisions |
3/3
HW3, HW3 Solution
Due Mon (3/6) at Noon |
Chap 17/18 |
| 8 |
3/7Midterm Review |
3/9
Midterm Exam 1, Midterm 1 solutions only |
|
|
| 9 |
3/13-3/17 - Spring Break - No Class |
| 10 |
3/21
Decision Trees |
3/23
Learning Theory |
|
Chap 18/19 |
| 11 |
3/28
Knowledge in Learning |
3/30
Bayesian Learning |
|
Chap 19/20 |
| 12 |
4/4
Neural Nets, SGD |
4/6
Undirected Graphical Models |
4/7
HW4 Due, HW4 Solution |
Chap 20 |
| 13 |
4/11
SGD for Undirected Graphs |
4/13
Unsupervised Learning, EM |
|
Chap 20/21 |
| 14 |
4/18
Passive Reinforcement Learning |
4/20
Active Reinforcement Learning |
4/21
HW5 Due, HW5 Solution |
Chap 21 |
| 15 |
4/25
Midterm Review |
4/27
Midterm Exam 2, Midterm 2 solutions only |
|
|
| 16 |
5/2
Project Presentations |
5/4
Project Presentations |
5/5
Project Due |
|
| 17 |
5/10 (Wed, 8:00 A.M. - 10:00 A.M. which is the scheduled final exam time)
Project Presentations |
Course Evaluation:
There will be five (5) homework assignments. Due dates for the
homeworks are strict: All homeworks must be received prior to their deadline
in order to receive credit. There will be no partial credit for late homeworks.
There will be two midterm exams held during lecture on Thursday, March 9 and Thursday, April 27. They will be closed book and closed notes.
There will be one final project due Friday, May 5. Note that this course is writing intensive. The requirements for a writing intensive course may be found at https://wwws.cs.umn.edu/faculty/teach/wr-index.html.
Grading:
| Homeworks |
30% (6% each) |
| Midterms |
40% (20% each) |
| Project |
30% |
Incompletes (or make up exams) will in general not be given. These options
will be considered only when a provably serious family or personal emergency
arises, proof is presented, and the student has already completed all but a
small portion of the work.
Scholastic Conduct
You must do your homeworks, programming assignments, and examinations
yourself---ON YOUR OWN.
Copying another's work, allowing (even negligently) others to
copy your work, or possession of electronic computing devices in the
testing area is cheating and grounds for penalties according to the
IT Bulletin.
|
