Department of Computer Science
Pomona College
CS 158 - Machine Learning
Fall 2016

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: David Kauchak
e-mail: [first_name][last_name]
office hours: Edmunds 224
  Tue. 11am-12
  Wed. 2:30-4pm
  Thu. 2:30-4:00pm
  and by appointment

time: T/Th 9:35-10:50pm
location: Edmunds 101 (Seaver Commons)
web page:


other resources:



Note: This is a tentative schedule and will likely change
8/30introduction (ppt)Ch 1-1.10Assignment 1
9/1decision trees (ppt)Tan Ch 4.3-4.3.5 
9/6geometric view of data (ppt)Ch 2 (2.4 optional)Assignment 2 (.tex)
9/8perceptron (ppt)Ch 3  
9/13features (ppt)Ch 4-4.4Assignment 3 (.tex)
9/15evaluation (ppt)Ch 4.5-4.8 
9/20imbalanced data (ppt)Ch 5-5.2Assignment 4 (.tex)
9/22beyond binary classification (ppt)Ch 5.3-5.4 
9/27gradient descent (ppt)Ch 6-6.5 (6.6 optional)Assignment 5 (.tex)
9/29regularization (ppt)  
10/4large margin classifiers (ppt)Ch 6.7Assignment 6 (.tex)
10/6SVM lab  
10/11probability basics (ppt)Optional: Movallen pgs 7-23 
10/13probabilistic models (ppt)Ch 7-7.5 
10/18NO CLASS  
10/20priors and logistic regression (ppt)Ch 7.6-7.7Assignment 7 (.tex)
10/25neural networks (ppt)Ch. 8 
10/27backpropagation (ppt)Optional: backprop example 
11/1deep learning (ppt)word vectors
tutorial module 1
Assignment 8 (.tex)
11/3hadoop basicstutorial module 2-2.4,2.6 
11/8map reducetutorial module 4Assignment 9 (.tex)
11/10map reduce 2tutorial module 5 
11/15no class - assignment 9 Final project
11/17ensemble learning (ppt)Ch 11-11.3 
11/22no class - final project  
11/24NO CLASS  
11/29k-means (ppt)Ch 2.4, 13-13.1 
12/1clustering (ppt)Ch 14-14.2 
12/6project presentations  

Exam schedule: