Department of Computer Science
Pomona College
CS 158 - Machine Learning
Fall 2023


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:
  Monday, 3-4pm
  Wednesday, 3-4pm
  Thursday, 2:30-4pm
  and by appointment

mentors: Eshaan Lumba, Saatvik Kher, Sam Malik
hours:
  Thursday, 7-9pm
  Friday, 6-8pm
  Sunday, 7-9pm


time: T/Th 1:15-2:30pm
location: Edmunds 114
web page: http://www.cs.pomona.edu/classes/cs158/

textbook:

Other information:

Schedule

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

Exam schedule: