ECE544NA: Pattern Recognition (Fall 2017)

Course Information

ECE 544NA is a special topics course in pattern recognition, and content varies every year. In Fall 2017, the course will cover three main areas, (1) disciminative models, (2) generative models, and (3) reinforcement learning models. 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 the students should be familiar with the underlying theory and software that are 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.

Instructor & TAs

Alexander Schwing

Email: aschwing[at]
Office Hour: Tues. 12:30-1:30PM
Room: CSL 103
Website: [link]

Raymond Yeh

Head Teaching Assistant
Email: yeh17[at]
Office Hour: Tues. 12:30-1:30PM
Room: ECEB 4034
Website: [link]

Teck Yian Lim

Teaching Assistant
Email: tlim11[at]
Office Hour: Monday 10:00-11:00AM
Room: ECEB 3034
Website: [link]

Safa Messaoud

Teaching Assistant
Email: messaou2[at]
Office Hour: Friday 4:00-5:00PM
Room: ECEB 4034
Website: [link]

Class Time & Location

Class Time: Tuesday, Thursday 11:00AM-12:20PM
Location: 2015 ECEB (map)

Course Discussions

Piazza for discussions: [link]
Compass for assignments: [link]

Homework & Projects

Homework: [link]
Project: [link]


Note:This syllabus is subject to change.

Lecture 1 August 29 Intro to Pattern Recognition [Slides]
Assignment 0 Assigned August 29 Assignment 0: Introduction + Python
[Assignment 0]
Assignment 1 Assigned August 29 Assignment 1: Binary Classification
[Assignment 1]
Project Assigned August 29 Final Project Instructions
Topics on Discriminative Models
Lecture 2 August 31 Linear Regression [Slides]
Lecture 3 September 5 Logistic Regression [Slides]
TA Lecture September 7 Machine Learning Pipeline + TensorFlow Intro (by Raymond) [Slides] [Ipython]
Lecture 4 September 12 Optimization Primal [Slides]
Lecture 5 September 14 Optimization Dual [Slides]
Assignment 0 Due September 14 Assignment 0: Introduction + Python [Assignment 0]
Lecture 6 September 19 Support Vector Machines [Slides]
Lecture 7 September 21 Multiclass classification and Kernel Methods [Slides]
Assignment 1 Due September 21 Assignment 1: Binary Classification [Assignment 1]
Assignment 2 Assigned September 21 Assignment 2: Deep Learning and Graphical Models for Image Denoising
[Assignment 2]
Lecture 8 September 26 Deep Neural Networks [Slides]
TA Lecture September 28 TensorFlow + Google Cloud [Slides]
Project Due September 28 Project: Proposal
Lecture 9 October 3 Structured Prediction (exhaustive search, dynamic programming) [Slides]
Lecture 10 October 5 Structured Prediction (ILP, LP relaxation, message passing, graph cut) [Slides]
Lecture 11 October 12 Conditional Random Fields and Structured SVMs (learning) [Slides]
Lecture 12 October 17 Deep Structured Methods (inference and learning) [Slides]
Assignment 2 Due October 19 Assignment 2: Deep Learning and Graphical Models for Image Denoising [Assignment 2]
Assignment 3 Assigned October 19 Assignment 3: Generative Models
[Assignment 3]
Topics on Generative Models
Lecture 13 October 19 K-Means [Slides]
Lecture 14 October 24 Gaussian Mixture Models [Slides]
Lecture 15 October 26 Expectation maximization/Majorize-Minimize/Concave-convex procedure [Slides]
Lecture 16 October 31 Structured Latent Variable Models (e.g., HMMs) [Slides]
Lecture 17 November 2 Variational Auto-encoders [Slides]
Lecture 18 November 7 Generative Adversarial Nets [Slides]
Lecture 19 November 9 Autoregressive Methods [Slides]
Assignment 3 Due November 9 Assignment 3: Generative Models [Assignment 3]
Assignment 4 Assigned November 9 Assignment 4: Reinforcement Learning
[Assignment 4]
Topics on Reinforcement Learning Models
Lecture 20 November 14 Markov Decision Processes [Slides]
Lecture 21 November 16 Q-learning [Slides]
No Lecture November 21 Thanksgiving Break [Slides]
No Lecture November 24 Thanksgiving Break [Slides]
Lecture 22 November 28 Policy Gradient [Slides]
Lecture 23 November 30 Actor-Critic [Slides]
Assignment 4 Due November 30 Assignment 4: Reinforcement Learning [Assignment 4]
Lecture 24 December 5 Final Exam Review [Slides]
Lecture 25 December 7 Final Project Presentations [Slides]
Lecture 36 December 12 Final Project Presentations [Slides]
Project Due Dec. 18 Project: Written Report + Code
Final Exam Dec. 18 Room: ECEB 2015 and ECEB 2017 [Slides]