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 tradeoffs 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 @ 35:00pm (15:0017:00)  via Zoom
Yinan's Office Hours: Fridays @ 10:30am  12:30pm (1030  1230)  via Zoom
D2L 580: https://d2l.arizona.edu/d2l/home/1355965
D2L 480: https://d2l.arizona.edu/d2l/home/1355962
Piazza: https://piazza.com/arizona/fall2023/csc480580/home
Instructor Homepage: http://www.pachecoj.com
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, KMeans 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, CrossValidation (slides)  CH 5  Practical Issues  
9/12  Practical Issues (continued)  Prediction Confidence, Statistical Tests, Bootstrap (slides)  CH 5  Practical Issues  
9/14  BiasVariance 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 Wrapup (slides) 