ECE544NA: Pattern Recognition (Fall 2018)

Course Information

ECE 544NA is a special topics course in pattern recognition, and content varies every year. In Fall 2018, 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.

Presentation Schedule: The final presentation schedule is available here.

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 33%, final project 33%, final exam 34%. Weights might get adjusted during the course.

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

Problem Sets: Download the problem sets here and the solution here.

Final Exam: **Two hour exam** Time: 8:00AM-11:00AM, Dec 19 2018. Room: ECEB 1013 & ECEB 1015. Please arrive in the assigned room (TBA) at 8:00AM on time.

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Instructor & TAs

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