Department of Computer Science
Pomona College
CS 158 - Machine Learning
Spring 2022

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]
office hours: TBA
  Mon - Thu: 2:30 - 3:30pm (via zoom, link in sakai)
  and by appointment

time: T/Th 1:15-2:30pm
location: SCOM 102 (Seaver Commons)
web page:


Administrative material


Note: This is a tentative schedule and will likely change
1/18introduction (ppt)Ch 1-2Assignment 1 (.tex)
1/21 @ 5pm
1/20decision trees (ppt)Tan Ch 4.3-4.3.5 
1/25geometric view of data (ppt)Ch 3 (3.4 optional)Assignment 2 (.tex)1/30 @ 11:59pm
1/27perceptron (ppt)Ch 4 
2/1features (ppt)Ch 5-5.4Assignment 3 (.tex)2/6 @ 11:59pm
2/3evaluation (ppt)Ch 5.5-5.9 
2/8imbalanced data (ppt)Ch 6-6.1Assignment 4 (.tex)2/13 @ 11:59pm
2/10beyond binary classification (ppt)Ch 6-6.3 
2/15gradient descent (ppt)Ch 7-7.5 (7.6 optional)Assignment 5 (.tex)2/20 @ 11:59pm
2/17regularization (ppt)  
2/22large margin classifiers (ppt)Ch 7.7Assignment 6 (.tex)3/1 @ 11:59pm
2/24SVM lab  
3/1probability basics (ppt)Optional: Movallen pgs 7-23 
3/3probabilistic models (ppt)Ch 9-9.5Assignment 7 (.tex)Part A: 3/6 @ 11:59
Part B: 3/11 @ 5pm
3/8priors and logistic regression (ppt)Ch 9.6-9.7
3/10neural networks (ppt)Ch 10 
3/15spring break
3/17spring break
3/22backpropagation (ppt)Optional: backprop exampleAssignment 8 (.tex)4/3 @ 11:59
3/24deep learning (ppt)word vectors
3/29deep learning 2 
3/31big data (ppt), hadoop  
4/5MapReduce Assignment 9 (.tex)Part A: 4/10 @ 11:59pm
Part B: 4/17 @ 11:59pm
4/7advanced MapReduce  
4/12final project discussionCh 13final project
4/14ensemble learning (ppt)  
4/19k-means (ppt)Ch 3.4, 15-15.1 
4/21clustering (ppt)Ch 16 
4/26ML ethicspaper 1, paper 2, article 1, article 2 
4/28work session  
5/3project presentations  

Exam schedule: