| Date: 4/21 | Title: Project presentation |
Four presentations per lecture: about 12 minutes each (10-minute presentation
and 2 minutes Q/A).
4/26 (Wed):
1. Chunseok and Bumsoon
2. Paul and Brandon
3. Amanda and Alethea
4. Jaerock
4/28 (Fri):
1. Neal and Mathew
2. Jeanho
3. Zane
4. SangEun
5/1 (Mon):
1. Meiqiu and Huei-Fang
2. Qingwu
3.
4.
5/2 (Tue):
1. Jaime and Josh
2. Cristian, Samarth, and Sumant
3. Thomas
4. Milan
5/3 (Wed): Makeup
1. Jiryang and Seungguk
2. Ramsey and Joel
3. Seung-Jin and Hyun-Jung
4. Jyothi
Remaining Teams:
|
| Date: 4/07 | Title: Mini project details |
Submit a hardcopy of your mini-project proposal by Monday 4/10,
in class. The proposal should contain:
- Names and emails of participants.
- Title of the project
- A short, one-paragraph description of the project.
Presentation and final report:
- Presentation:
- will be roughly 15 minutes for each team.
- You may present preliminary results.
- Final report:
- 5 to 6 pages, in a conference paper format
of your choice. Single-spaced.
- Due 2 days after the final exam (final exam is 5/8, 10:30am-12:30pm).
|
| Date: 4/04 | Title: HW3 corrections and clarifications |
1. For problem 2, use the same grid and reward as problem 1.
2. For problem 2 in general, checking for convergence in the way it
is described may give mixed results. You may
a. set a fixed max number of iterations and run all three policies
b. compare the resulting Q(s,a) and the analytical results.
3. For the probabilistic policy in problem 2 (item 3), the two k values
should be 2 and 5, not 1 and 5.
4. For problem 3, the subscript "t" represents the t-th visit to
(s,a).
|
| Date: 2/15 | Title: HW1 plotting the error curve |
| Producing the error plot seems to be an issue. You may omit the intermediate
steps, and just report the final error on the training set and the test set.
|
| Date: 2/10 | Title: Dealing with missing attribute values |
> I am having a little problem in finishing up the homework.
> It's working fine with the 'PlayTennis' data, but not with the 'Breast
> Cancer' data. I found out this is because of the case when Attributes or
> Examples_vi is empty(line 8 and 16 of the ID3 pseudocode in the textbook
> page 56.)
> In those cases, it says to add a node with label='most common value of
> Target_attribute in Examples'. This is not + or -, but one of the values of
> the attributes, then how can it make a decision in this case? And what is
> the 'most common' value of the attributes?
There can be several strategies. You can simply replace the
missing attribute value with the most commonly occurring
value for that particular attribute. This can be done in some kind
of preprocessing stage of the data.
|
| Date: 2/09 | Title: HW1 clarification |
Hi everyone,
For problem 1, item 2, you don't need to provide a formal proof. Just state your
intuition.
As for the breast cancer database, you may experience problem if you copy and paste
the url from the pdf file.
Use this url in that case:
http://www.ics.uci.edu/~mlearn/databases/breast-cancer-wisconsin/
Yoonsuck
|