CSC 580: Principles of Machine Learning (Fall 2022)
Students will learn why machine learning is a fundamentally different way of writing computer programs from traditional programming, and why this is often an attractive way of solving practical problems. Machine learning is about automatic ways for computers to collect and/or adapt to data to make predictions, make decisions, or gain insight; students will learn both advantages and unique risks that this approach offers. They will learn the fundamental frameworks, computational methods, and algorithms that underlie current machine learning practice, and how to derive and implement many of them.
Logistics info
Time and venue: MW 3:30pm-4:45pm, Gould-Simpson 701
Piazza link; Access Code: learning
Gradescope entry code: XVXNBZ
D2L course webpage: lecture video recordings are at “UA Tools” -> “Zoom”
We will be using Piazza to make important announcements and do Q&As. Some general rules:
- If you have technical questions, try posing your questions as general as possible, to promote discussions among the class.
- If you have private questions, generally please make a private Piazza post instead of sending me an email - This will help facilitate my processing of your requests significantly.
Instructor
Gould-Simpson 720
Office Hour: Tuesdays 4-5pm Wednesdays 11am-12pm Mondays 5-6pm at my office or by appointment
Textbook
The required textbook is Hal Daumé’s A Course in Machine Learning, freely available online.
We recommended the following texts for optional reading:
Shai Shalev-Shwartz and Shai Ben-David, Understanding machine learning: from theory to algorithms
Moritz Hardt and Benjamin Recht, Patterns, predictions, and actions: Foundations of machine learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The elements of statistical learning (2nd edition).
Tom Mitchell, Machine Learning.
Kevin Murphy, Machine learning: a probabilistic perspective.
Richard Duda, Peter Hart, and David Stork, Pattern classification (2nd edition).
Review for prerequisites
Here are some excellent notes for probability review and linear algebra review from Stanford’s CS 229 course.
See also The matrix cookbook, The Probability and Statistics Cookbook, and Calculus cheatsheet (recommended by Prof. Kwang-Sung Jun).
LaTeX
You may find using LaTeX helpful in writing homeworks or reports. Some useful LaTeX resources: Learn LaTeX in 30 minutes by Overleaf; Introduction to LATEX by MIT Research Science Institute
Machine learning courses at UA
CSC 535 Probabilistic Graphical Models by Kobus Barnard
ISTA 457/INFO 557 Neural Networks by Steven Bethard
CSC 665 Online Learning and Multi-armed Bandits by Kwang-Sung Jun
INFO 521 Introduction to Machine Learning by Clayton Morrison
CSC 665 Advanced Topics in Probabilistic Graphical Models by Jason Pacheco
CSC 580 Principles of Machine Learning by Carlos Scheidegger
MIS 601 Statistical Foundations of Machine Learning by Junming Yin
MATH 574M Statistical Machine Learning by Helen Zhang
CSC 696H: Topics in Reinforcement Learning Theory by Chicheng Zhang