Description of Course

Students will learn why machine learning is a fundamentally different way of writing computer programs, and why this approach is often a uniquely attractive way of solving practical problems. Machine learning is all about automatic ways for computers to find patterns in datasets; students will learn both advantages and unique risks that this approach offers. They will learn the fundamental computational methods, algorithms, and perspectives which underlie current machine learning methods, and how to derive and implement many of them. Students will learn the fundamentals of unsupervised and supervised machine learning methods, the computational and quality trade-offs between different methods, and how to adapt existing methods to fit their own research needs.


We will use the following textbook, which is freely available online:
         Daumé, Hal. "A Course in Machine Learning." 2017.
The full textbook can also be downloaded as a PDF if you prefer that format. All reading assignments will be posted in the schedule below.

Instructor and Contact Information:

Instructor: Jason Pacheco, GS 724, Email:
Jason's Office Hours: Fridays @ 3-5:00pm (15:00-17:00) - via Zoom
Yinan's Office Hours: Fridays @ 10:30am - 12:30pm (1030 - 1230) - via Zoom
D2L 580:
D2L 480:
Instructor Homepage:

Date Topic Readings Assignment
8/22 Introduction + Course Overview   (slides) W3Schools : Numpy Tutorial
YouTube : Numpy Tutorial : Mr. P Solver
HW0: Calibration
(Due: 8/29 @ 12pm noon)
8/24 Basics - Decision Trees, Learning Algorithms   (slides) CH 1 - Decision Trees
8/29 Limits - Optimal Bayes Rate Classifier, Overfitting / Underfitting   (slides) CH 2 - Limits of Learning
8/31 Geometry - Nearest Neighbor Classifiers, K-Means Clustering   (slides) CH 3 - Geometry and Nearest Neighbors
9/5 The Perceptron Algorithm   (slides) CH 4 - The Perceptron   (Prof. Surdeanu slides) HW1 (Due: 9/15)
9/7 Practical Issues - Performance measures, overfitting / underfitting, Cross-Validation   (slides) CH 5 - Practical Issues
9/12 Practical Issues (continued) - Prediction Confidence, Statistical Tests, Bootstrap   (slides) CH 5 - Practical Issues
9/14 Bias-Variance Decomposition and Friends   (slides)
9/19 Linear Models: Linear Regression   (slides) CH 7 - Linear Models
9/21 Linear Models: Logistic Regression   (slides)
9/26 Nonlinear Models: Basis Functions, Kernels, SVM   (slides)
9/28 Probability, Naive Bayes, Graphical Models   (slides) CH 9 - Probabilistic Modeling HW2 (Due: 10/8)
10/3 Probability, Naive Bayes, Graphical Models (continued)   (slides)
10/5 Probability, Naive Bayes, Graphical Models (continued)   (slides)
10/10 Midterm Review   (slides)
10/12 Midterm Exam Midterm Exam
10/17 Bias and Fairness   (slides) CH8 - Bias and Fairness
10/19 Unsupervised Learning   (slides) CH15 - Unsupervised Learning
10/24 Unsupervised Learning (Cont'd)   (slides)
10/26 Gaussian Mixture Models and Expectation Maximization   (slides)
10/31 Neural Networks and Backpropagation   (slides) CH 10 - Neural Networks
11/2 Neural Networks and Backpropagation (continued)   (slides)
11/7 Ensemble Methods   (slides) CH 13 - Ensemble Methods
Note: We skipped CH 12!
11/9 Reinforcement Learning   (slides)
11/14 Reinforcement Learning (Cont'd)   (slides)
11/16 Reinforcement Learning (Cont'd)   (slides)
11/21 Data Visualization and Summarization   (slides)
11/23 Thanksgiving Break - No Class
11/28 Data Visualization and Summarization   (slides)
11/30 Final Exam Review   (slides)
12/5 Course Wrap-up   (slides)

© Jason Pacheco, 2020