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EECS 453: Principles of Machine Learning
Instructor: Prof. Laura Balzano, Prof. Qing Qu, Prof. Lei Ying
Prerequisite
EECS 351, or EECS 301, or any linear algebra courses
Coverage
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.
This is an entry-level machine learning course targeted for senior undergraduate EE and CE students, and junior master students outside the area of Signal and Image Processing & Machine Learning. Compared to EECS 445, this course places slightly greater emphasis on mathematical principles and is better suited for students who have limited experience with programming and machine learning.
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
Assessment: (i) 5 homework assignments (40%), (ii) mid-term exam (30%), (iii) course projects (25%), (iv) participation & course evaluation (5%)
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
Course Syllabus (Note: the schedule is tentative, and is subject to change during the semester.)
| Week | Date | Topic | Contents | Homework, Review |
| Week-1-1 | 08/29 | Introduction (Remote) | Course overview | |
| Week-1-2 | 08/31 | Supervised Learning (Remote) | Introduction to supervised learning, linear models, regularization | Linear Algebra Review |
| Week-2-1 | 09/05 | Labor Day | No class | |
| Week-2-2 | 09/07 | Supervised Learning | Learning Theory | Probability Review, HW1 Release |
| Week-3-1 | 09/12 | Supervised Learning | Linear regression I | |
| Week-3-2 | 09/14 | Supervised Learning | Linear regression II | Python Review |
| Week-4-1 | 09/19 | Supervised Learning | Linear Classifiers | |
| Week-4-2 | 09/21 | Supervised Learning | Linear Discriminant Analysis | HW1 Due, HW2 Release |
| Week-5-1 | 09/26 | Supervised Learning (remote) | Logistic regression | |
| Week-5-2 | 09/28 | Supervised Learning (remote) | Optimization methods I | |
| Week-6-1 | 10/03 | Supervised Learning | Optimization methods II | |
| Week-6-2 | 10/05 | Supervised Learning | Support vector machine (SVM) I | HW2 Due, HW3 Release |
| Week-7-1 | 10/10 | Supervised Learning | Support vector machine (SVM) II | |
| Week-7-2 | 10/12 | Supervised Learning | Support vector machine (SVM) III | |
| Week-8-1 | 10/17 | Fall Study Day | No class | |
| Week-8-2 | 10/19 | Supervised Learning | Dual SVM | HW3 Due |
| Week-9-1 | 10/24 | Supervised Learning | Nonlinear models, kernel methods | |
| Week-9-2 | 10/26 | Supervised Learning | Introduction to deep neural networks I | |
| Week-10-1 | 10/31 | Supervised Learning | Introduction to deep neural networks II | |
| Week-10-2 | 11/02 | Supervised Learning | Introduction to deep neural networks III | |
| Week-11-1 | 11/07 | Midterm | Midterm | |
| Week-11-2 | 11/09 | Unsupervised Learning | Introduction to unsupervised learning, clustering problem, K-means | Project Proposal Due, HW4 Release |
| Week-12-1 | 11/14 | Unsupervised Learning | K-means, mixtures of Gaussian, expectation maximization | |
| Week-12-2 | 11/16 | Unsupervised Learning | Dimension reduction, PCA | |
| Week-13-1 | 11/21 | Unsupervised Learning | Dimension reduction II | |
| Week-13-2 | 11/23 | Thanksgiving | No Class | |
| Week-14-1 | 11/28 | Unsupervised Learning (Remote) | Representation learning, matrix factorization | HW4 Due, HW5 Release |
| Week-14-2 | 11/30 | Unsupervised Learning (Remote) | Autoencoder & self-supervised learning | |
| Week-15-1 | 12/05 | Unsupervised Learning | Generative Models | HW5 Due |
| Week-15-2 | 12/07 | Final Presentation |