Department of Computer Science
Pomona College
CS 158 - Machine Learning
Fall 2019


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]@pomona.edu
office hours: Edmunds 224
  Mon 10am-12
  Wed 2:30-3:30pm
  Fri 10am-12
  and by appointment


time: T/Th 1:15-2:30pm
location: SCOM 103 (Seaver Commons)
web page: http://www.cs.pomona.edu/classes/cs158/

textbook:

other resources:


Schedule

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

Exam schedule: