instructor: Dave Kauchak
e-mail: [first_name][last_name]@pomona.edu
office hours:
Mon: 10-11am
Tue: 10-11am
Thu: 10-11am, 4-5pm
and by appointment
Mentor hours: Wed 6-8pm (Alan, Edmunds downstairs)
time: T/Th 2:45-4pm
location: SCOM 102
web page: http://www.cs.pomona.edu/classes/cs158/
textbook:
Other information:
| Date | Topic | Reading | Assignment | Due |
|---|---|---|---|---|
| 8/26 | introduction (ppt) | Ch 1-2 | Assign 1 (.tex) | 8/29 @ 5pm |
| 8/28 | decision trees (ppt) | Tan Ch 4.3-4.3.5 | ||
| 9/2 | geometric view of data (ppt) | Ch 3 (3.4 optional) | Assign 2 (.tex) | 9/7 @ 11:59pm |
| 9/4 | perceptron (ppt) | Ch 4 | ||
| 9/9 | features (ppt) | Ch 5-5.4 | Assign 3 (.tex) | 9/14 @ 11:59pm |
| 9/11 | evaluation (ppt) | Ch 5.5-5.9 | ||
| 9/16 | imbalanced data (ppt) | Ch 6-6.1 | Assign 4 (.tex) | 9/21 @ 11:59pm |
| 9/18 | beyond binary (ppt) | Ch 6.2-6.3 | ||
| 9/23 | gradient descent (ppt) | Ch 7-7.5 (7.6 optional) | Assign 5 (.tex) | 9/28 @ 11:59pm |
| 9/25 | regularization (ppt) | |||
| 9/30 | large margin classifiers (ppt) | Ch 7.7 | Assign 6 (.tex) | 10/10 @ 11:59pm |
| 10/2 | probability basics (ppt) | Movallen pgs 7-23 (optional) | ||
| 10/7 | probabilistic models (ppt) | Ch 9-9.5 | ||
| 10/9 | No class | |||
| 10/14 | Fall break | |||
| 10/16 | priors and logistic regression (ppt) | Ch 9.6-9.7 | Assign 7 (.tex) | A: 10/19 @ 11:59pm B: 10/26 @ 11:59pm |
| 10/21 | neural networks (ppt) | Ch 10 | ||
| 10/23 | backpropogation (ppt) | backprop example (optional) | ||
| 10/28 | deep learning (ppt) | word vectors | Assign 8 (.tex) | 11/2 @ 11:59pm |
| 10/30 | big data (ppt), hadoop | |||
| 11/4 | MapReduce | Assign 9 | 11/9 @ 11:59pm | |
| 11/6 | MapReduce conclusions, final project discussion | |||
| 11/11 | ensemble learning | Ch 13 | final project | |
| 11/13 | k-means | Ch 3.4, 15-15.1 | ||
| 11/18 | clustering | Ch 16 | ||
| 11/20 | ML ethics | |||
| 11/25 | final project work session | |||
| 11/27 | Thanksgiving | |||
| 12/2 | Project presentations |
Exam schedule: