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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 class is an entry-level undergraduate course in machine learning mainly targeted for senior undergraduate and master students in the ECE program. This course could have a significant overlap with EECS 445, but with more emphasis on mathematical principles.

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:

  • Basics of probability, linear algebra, and optimization
  • Dimension reduction: PCA and kernel PCA
  • Unlock relations: ICA and CCA
  • Clustering (spectral, hierarchical)
  • Generative models (GMM, GAN, variational autoencoder)
  • Regression and linear prediction
  • Support vector machines and kernel methods
  • Neural networks
  • Online/reinforcement learning

Assessment: (i) homework assignment (bi-monthly, 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.

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