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EECS 553: Machine Learning (ECE)
Instructor: Prof. Laura Balzano, Prof. Clayton Scott, Prof. Al Hero
Coverage
The goal of machine learning is to develop computer algorithms that can learn from data or past experience to predict well on the new unseen data. In the past few decades, machine learning has become a powerful tool in artificial intelligence and data mining, and it has made major impacts in many real-world applications.
This course will give a graduate-level introduction of machine learning and provide foundations of machine learning, mathematical derivation and implementation of the algorithms, and their applications.
In addition to mathematical foundations, students will implement several machine learning algorithms in Python and apply them to a variety of data sets spanning several applications. This involves an open-ended project.
Advisory Prerequisite
Graduate coursework in probability and linear algebra.
Textbook(s)
Bishop, Christopher M. Pattern Recognition and Machine Learning. New York, NY: Springer, 2006.
General topics
- supervised learning
- unsupervised learning
- learning theory
- graphical models
- reinforcement learning
- sparsity and feature selection
- Bayesian techniques
- deep learning