Department of Computer Science
Pomona College
CS 158 - Machine Learning
Fall 2025


Machine learning focuses on discovering patterns in and learning from data. This course is an introduction to the most common problems in machine learning and to the techniques used to tackle these problems. The course will focus not only on how and when to use particular approaches, but also the details of how those approaches work.

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:

Schedule

Note: This is a tentative schedule and will likely change
DateTopicReadingAssignmentDue
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 Ch 9.6-9.7
10/21 neural networks Ch 10
10/23 backpropogation backprop example (optional)
10/28 deep learning word vectors
10/30 TBD
11/4 TBD
11/6 TBD
11/11 ensemble learning Ch 13
11/13 k-means Ch 3.4, 15-15.1
11/18 clustering Ch 16
11/20 TBD
11/25 TBD
11/27 Thanksgiving
12/2 Project presentations

Exam schedule: