Machine learning is about automatic ways for computers to collect and/or adapt to data to make predictions, make decisions, or gain insight. It can also be seen as a fundamentally different way of writing computer programs from traditional programming, which is often an attractive way of solving practical problems. Students will learn the fundamental frameworks, computational methods, and algorithms that underlie current machine learning practice, and how to derive and implement many of them. They will also learn both advantages and unique risks that this approach offers.
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.
We will also use the following textbook, which is freely available online:
Hardt, Moritz and Recht, Benjamin. "Patterns, Predictions, and Actions: A Story About Machine Learning." 2022.
Instructor: Jason Pacheco, GS 707, pachecoj@cs.arizona.edu
TA: Emiliano Islas, eislasq@arizona.edu
TA: Mahshad Habibpourparizi, mahshadh@arizona.edu
D2L 580: https://d2l.arizona.edu/d2l/home/1815948
D2L 480: https://d2l.arizona.edu/d2l/home/1815945
Piazza: https://piazza.com/arizona/fall2026/csc480580
Gradescope 580: https://www.gradescope.com/courses/1364022
Gradescope 480: https://www.gradescope.com/courses/1364018
Instructor Homepage: http://www.pachecoj.com
| Date | Topic | Readings | Assignment |
|---|---|---|---|
8/24
|
Introduction + Course Overview (slides) |
W3Schools : Numpy Tutorial YouTube : Numpy Tutorial : Mr. P Solver |
HW0: Calibration |
| 8/26 | Basics - Decision Trees, Learning Algorithms (slides) | CH 1 - Decision Trees |
|
| 8/31 | Limits - Optimal Bayes Rate Classifier / Overfitting / Underfitting (slides) | CH 2 - Limits of Learning | |
| 9/02 | Geometry, Nearest Neighbor Classifiers, K-Means (slides) | CH 3 - Geometry and Nearest Neighbors | HW1 (Due 9/15) |
| 9/07 | Labor Day : No Classes | ||
| 9/09 | Practical Issues - Performance measures, CV, Pred. Conf. | CH 5 - Practical Issues | |
| 9/14 | Practical Issues (Continued) | CH 5 - Practical Issues | |
| 9/16 | Linear Models - Regression | CH 7 - Linear Models | |
| 9/21 | Linear Models - Classification | CH 7 - Linear Models | HW2 (Due 9/29) |
| 9/23 | Linear Models (Continued) | CH 7 - Linear Models | |
| 9/28 | Linear Models (Continued) | CH 7 - Linear Models | |
| 9/30 | Nonlinear Models | Project Proposals | |
| 10/05 | Nonlinear Models (Continued) | ||
| 10/07 | Unsupervised Learning | ||
| 10/12 | Unsupervised Learning (Continued) | ||
| 10/14 | Midterm Exam | Midterm | |
| 10/19 | |||
| 10/21 | |||
| 10/26 | |||
| 10/28 | |||
| 11/02 | |||
| 11/04 | |||
| 11/09 | |||
| 11/11 | Veteran's Day : No Classes | ||
| 11/16 | |||
| 11/18 | |||
| 11/23 | |||
| 11/25 | |||
| 11/30 | |||
| 12/02 | |||
| 12/07 | |||
| 12/09 | Final Lecture |