**EECS 498: Principles of Machine Learning, Fall 2021**

**Course** **Instructor:** Prof. Laura Balzano, Prof. Qing Qu, Prof. Lei Ying

(listed in alpha beta order, please contact Prof. Qing Qu for more details)

**Teaching Assistant:** TBA

**Title: **Principles of Machine Learning

**Course Time:** Mon/Wed 1:30 PM – 3:00 PM, 3 credit hour

**Office Hour**: TBA

**Prerequisite:** EECS 351, EECS 301, or other linear algebra courses

**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 (weekly, 40%), (ii) mid-term exam (30%), (iii) Final exam (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.

**Related courses**:

- 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