EECS 498: Principles of Machine Learning, Fall 2022
(listed in alpha beta order, please contact Prof. Qing Qu for more details)
Teaching Assistant: Alexander Ritchie
Title: Principles of Machine Learning
Course Time: Mon/Wed 3:00 PM – 4:30 PM, 3 credit hour
Office Hour: Wed 3:30 PM – 5:00 PM
Notice: This is an entry-level machine learning course targeted for senior undergraduate and junior master students. This course is a little bit more emphasis on mathematical principles in comparison to EECS 445. Students outside the ECE program interested in machine learning are welcome as well!
Overview: The class will cover basic principles in machine learning, such as unsupervised learning (e.g., clustering, mixture models, dimension reduction), supervised learning (e.g., regression, classification, neural networks & deep learning), and reinforcement learning. For each topic, key algorithmic ideas/intuitions and basic theoretical insights will be highlighted.
Course Materials: slides and videos will be accessed via Canvas (TBA). Tentative topics that will be covered in this course are supervised learning, unsupervised learning, and reinforcement learning:
- Basics of probability, linear algebra, and optimization
- Regression and linear prediction
- Support vector machines and kernel methods
- Deep neural networks
- Dimension reduction: PCA, autoencoder
- Clustering (Kmeans, Mixture of Gaussians, EM)
- Representation learning: nonnegative matrix factorization, dictionary learning
- Basics of Online/reinforcement learning
Assessment: (i) homework assignment (5 in total, 40%), (ii) mid-term exam (30%), (iii) Final project (30%)
Textbook: We recommend the following books and articles, although we will not follow them closely.
- Foundations of Machine Learning, by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
- Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
- Mathematics for Machine Learning, by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.
- Linear Algebra and Optimization for Machine Learning, by Charu C. Aggarwal.
- EECS 445. Introduction to Machine Learning
- EECS 453. Applied Matrix Algorithms for Signal Processing, Data Analysis, and Machine Learning
- EECS 505. Computational Data Science and Machine Learning
- EECS 545. Machine Learning
|Week-1-1||08/30||Introduction||Course overview||Qing Qu|
|Week-1-2||09/01||Supervised Learning||Introduction to supervised learning, linear models, regularization||Qing Qu||Linear Algebra Review|
|Week-2-1||09/06||Labor Day||No class|
|Week-2-2||09/08||Supervised Learning||Logistic Regression||Qing Qu||Probability Review, HW1 Release|
|Week-3-1||09/13||Supervised Learning||Logistic Regression continued, optimization methods||Qing Qu|
|Week-3-2||09/15||Supervised Learning||Optimization methods||Qing Qu||Python Review|
|Week-4-1||09/20||Supervised Learning||Linear classification, support vector machine (SVM) I||Qing Qu|
|Week-4-2||09/22||Supervised Learning||Linear classification, support vector machine (SVM) II||Qing Qu||HW1 Due, HW2 Release|
|Week-5-1||09/27||Supervised Learning||Dual SVM||Qing Qu|
|Week-5-2||09/29||Supervised Learning||Nonlinear models, kernel methods||Qing Qu|
|Week-6-1||10/04||Supervised Learning||Introduction to deep neural networks I||Qing Qu|
|Week-6-2||10/06||Supervised Learning||Introduction to deep neural networks II||Qing Qu||HW2 Due, HW3 Release|
|Week-7-1||10/11||Supervised Learning||Introduction to deep neural networks III||Qing Qu|
|Week-7-2||10/13||Unsupervised Learning||Introduction to unsupervised learning, clustering problem, K-means||Qing Qu|
|Week-8-1||10/18||Fall Study Day||No class|
|Week-8-2||10/20||Unsupervised Learning||K-means, mixtures of Gaussian, expectation maximization||Qing Qu||HW3 Due|
|Week-9-1||10/25||Midterm Review||Midterm Review||Qing Qu|
|Week-10-1||11/01||Unsupervised Learning||Dimension reduction, PCA||Qing Qu|
|Week-10-2||11/03||Unsupervised Learning||Dimension reduction II||Qing Qu||HW4 Release, Project Proposal Due|
|Week-11-1||11/08||Unsupervised Learning||Representation learning, matrix factorization||Qing Qu|
|Week-11-2||11/10||Unsupervised Learning||Representation learning, matrix factorization||Qing Qu|
|Week-12-1||11/15||Unsupervised Learning||Autoencoder, self-supervised learning, GAN||Qing Qu|
|Week-12-2||11/17||Unsupervised Learning||Autoencoder, self-supervised learning, GAN||Qing Qu||HW4 Due, HW5 Release|
|Week-13-1||11/22||Reinforcement Learning||TBD||Lei Ying|
|Week-14-1||12/01||Reinforcement Learning||TBD||Lei Ying|
|Week-14-2||12/03||Reinforcement Learning||TBD||Lei Ying||HW5 Due|
|Week-15-1||12/06||Final||Final project preparation||Qing Qu|
|Week-15-2||12/08||Final||Final project presentation||Qing Qu|