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: Jason Pacheco, GS 724, Email: pachecoj@cs.arizona.edu Jason's Office Hours: Fridays @ 3-5:00pm (15:00-17:00) - via Zoom Yinan's Office Hours: TBD D2L 580: https://d2l.arizona.edu/d2l/home/1571901 D2L 480: https://d2l.arizona.edu/d2l/home/1571904 Piazza: https://piazza.com/arizona/spring2025/csc480580 Instructor Homepage: http://www.pachecoj.com
| Date | Topic | Readings | Assignment |
|---|---|---|---|
1/16
|
Introduction + Course Overview (slides) |
W3Schools : Numpy Tutorial YouTube : Numpy Tutorial : Mr. P Solver |
HW0: Calibration (Due: 1/23) |
| 1/21 | Basics - Decision Trees, Learning Algorithms (slides) | CH 1 - Decision Trees | |
| 1/23 | Limits - Optimal Bayes Rate Classifier, Overfitting / Underfitting (slides) | CH 2 - Limits of Learning | |
| 1/28 | Geometry - Nearest Neighbor Classifiers, K-Means Clustering (slides) | CH 3 - Geometry and Nearest Neighbors | HW1 (Due: 2/7) |
| 1/30 | Practical Issues - Performance measures, overfitting / underfitting, Cross-Validation (slides) | CH 5 - Practical Issues | |
| 2/4 | Practical Issues (continued) - Prediction Confidence, Statistical Tests, Bootstrap (slides) | CH 5 - Practical Issues | |
| 2/6 |
Linear Models: Linear Regression (slides) |
CH 7 - Linear Models |
|
| 2/11 | Linear Models: Logistic Regression (slides) | CH 7 - Linear Models | |
| 2/13 | TBD |
|
HW2 (Due: 2/24) |
| 2/18 | The Perceptron Algorithm (slides) | CH 4 - The Perceptron (Prof. Surdeanu slides) | |
| 2/20 | Nonlinear Models (slides) | ||
| 2/25 |
Nonlinear Models (continued) (slides) |
Project Proposals (Due: 3/18) | |
| 2/27 | TBD | ||
| 3/4 | TBD | ||
| 3/6 | Midterm Exam | Midterm Exam | |
| 3/11 | Spring Recess - No Class! | ||
| 3/13 | Spring Recess - No Class! | ||
| 3/18 |
Probability, Naive Bayes, Graphical Models (slides) |
CH 9 - Probabilistic Modeling |
|
| 3/20 |
Probability, Naive Bayes, Graphical Models (continued) (slides) |
CH 9 - Probabilistic Modeling | |
| 3/25 | Neural Networks (NNs) and Backpropagation (slides) | CH 10 - Neural Networks | HW3 (Due: 4/4) |
| 3/27 | Neural Networks : Convolutional NNs (slides) | ||
| 4/1 | Neural Networks : Autoencoders (slides) | ||
| 4/3 |
Unsupervised Learning (slides) |
CH15 - Unsupervised Learning |
|
| 4/8 | Unsupervised Learning (continued) (slides) | CH15 - Unsupervised Learning | HW4 (Due: 4/18) |
| 4/10 |
Ensemble Methods (slides) |
CH 13 - Ensemble Methods | |
| 4/15 | Large Language Models (LLMs) | ||
| 4/17 | LLMs (continued) | ||
| 4/22 | Reinforcement Learning (slides) | ||
| 4/24 | Reinforcement Learning (continued) (slides) | ||
| 4/29 | TBD | ||
| 5/1 | TBD | ||
| 5/6 | Course Wrap-up (slides) | Final Projects Due |