ECE544NA: Pattern Recognition (Fall 2019)

Course Information

ECE 544NA is a special topics course in pattern recognition, and content varies every year. In Fall 2019, the course will cover three main areas, (1) disciminative models, (2) generative models, and (3) reinforcement learning. See course syllabus for more details.

The goal of the course is to provide an understanding of recent research topics in pattern recognition. After having completed the class, students should be familiar with the underlying theory and software that is frequently used in publications related to pattern recognition.

Pre-requisites: Probability, linear algebra, and proficiency in Python, MATLAB or equivalent.

Recommended Text: (1) Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville, (2) Pattern Recognition and Machine Learning by Christopher Bishop, (3) Graphical Models by Nir Friedman and Daphne Koller and (4) Machine Learning: A Probabilistic Perspective by Kevin Murphy.

Grading: Scribing/project 1/3, midterm 1/3 and final exam 1/3 of the grade. Weights might get adjusted during the course.

Late Policy: No late submission will be accepted after the due date.

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Instructor & TAs

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