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.

Instructor & TAs

Alexander Schwing

Email: aschwing[at]
Office Hour: Tues. 13:00-14:00PM
Room: CSL 103
Website: [link]

Safa Messaoud

Teaching Assistant
Email: messaou2[at]
Office Hour: Friday 9:00AM-10:00AM
Room: ECEB 4034

Raymond A. Yeh

Teaching Assistant
Email: yeh17[at]
Office Hour: Friday 9:00AM-10:00AM
Room: ECEB 4034
Website: [link]

Class Time & Location

Class Time: Tuesday, Thursday 11:00AM-12:20PM
Location: 3017 ECEB (map)

Course Discussions

Piazza for discussions: [link]
GradeScope for assignments (self-enrollment code 9N2G22): [link]

Scribe & Project

Detailed Instructions: [link]


The syllabus is subject to change.

Lecture 1 Aug. 27 Intro/Nearest Neighbor [Slides] [Rec] [Audio]
Lecture 2 Aug. 29 Linear Regression [Slides] [Rec] [Audio]
Lecture 3 Sep. 3 Logistic Regression [Slides] [Rec] [Audio]
Lecture 4 Sep. 5 Optimization Primal [Slides] [Rec] [Audio]
Lecture 5 Sep. 10 Optimization Dual [Slides] [Rec] [Audio]
Lecture 6 Sep. 12 SVM [Slides] [Rec] [Audio]
Lecture 7 Sep. 17 Multiclass Classification and Kernel Methods [Slides] [Rec] [Audio]
Lecture 8 Sep. 19 Deep Nets 1 (Layers) [Slides] [Rec] [Audio]
Lecture 9 Sep. 24 Deep Nets 2 (Backprop) & Pytorch 1 [Slides] [Rec] [Audio]
Lecture 10 Sep. 26 Pytorch 2 [Slides] [Rec] [Audio]
Lecture 11 Oct. 1 Review [Slides] [Rec] [Audio]
Midterm Oct. 3 Midterm [Slides] [Rec] [Audio]
Lecture 13 Oct. 8 Ensemble Methods (Boosting/Random Forest/Deep Nets) & Regularization/Cross-Validation [Slides] [Rec] [Audio]
Lecture 14 Oct. 10 Structured Prediction (exhaustive search, dynamic programming) [Slides] [Rec] [Audio]
Lecture 15 Oct. 15 Structured Prediction (ILP, LP relaxation) [Slides] [Rec] [Audio]
Lecture 16 Oct. 17 Learning in Structured Models [Slides] [Rec] [Audio]
Lecture 17 Oct. 22 Learning Theory [Slides] [Rec] [Audio]
Lecture 18 Oct. 24 PCA and SVD [Slides] [Rec] [Audio]
Lecture 19 Oct. 29 k-Means [Slides] [Rec] [Audio]
Lecture 20 Oct. 31 Gaussian Mixture Models [Slides] [Rec] [Audio]
Lecture 21 Nov. 5 Expectation Maximization [Slides] [Rec] [Audio]
Lecture 22 Nov. 7 Hidden Markov Models [Slides] [Rec] [Audio]
Lecture 23 Nov. 12 Variational Auto-Encoders [Slides] [Rec] [Audio]
Lecture 24 Nov. 14 Generative Adversarial Nets [Slides] [Rec] [Audio]
Lecture 25 Nov. 19 Autoregressive Methods, Recurrent Neural Nets, Graph Convolutions [Slides] [Rec] [Audio]
Lecture 26 Nov. 21 MDP [Slides] [Rec] [Audio]
No Lecture Nov. 26 Fall Break [Slides]
No Lecture Nov. 28 Fall Break [Slides]
Lecture 27 Dec. 3 Q-Learning [Slides] [Rec] [Audio]
Lecture 28 Dec. 5 Policy Gradient [Slides] [Rec] [Audio]
Lecture 29 Dec. 10 Review [Slides] [Rec] [Audio]
Final Exam Dec. 13 Final Exam, Rooms 2015/3017 [Slides]