CS59200-TMP: Topics in Machine Perception (Fall 2022)

Images generated from DALL-E-2 with text prompt A robot learning to perceive the world

Course Information

This course covers the concepts and techniques for conducting research in the area of machine perception, i.e., how to enable machines to sense the world (with focus on deep learning methods with computer vision applications). The lectures are designed to lead discussions and facilitate student presentations on selected advanced topics in the area. We will cover three main areas, (1) predictive models, (2) generative models, and (3) other recent advances. The course aims to develop students' knowledge and analysis capabilities for understanding research publications in machine perception, e.g., papers from CVPR, ICCV, ECCV, NeurIPS, etc.

Pre-requisites:

Textbooks (Optional):

Grading:


Instructor

Raymond A. Yeh

Time & Location

  • Time: Tuesday, Thursday 10:30AM-11:45AM
  • Location: LWSN B134

  • Office Hour: 12:00PM-01:00PM
  • Office Hour Location: LWSN 1192

Other Materials


Course Schedule

DateTypeDescriptionMaterials
--- Topic Basics

Select from the following:
Aug. 23 Lecture 1 Intro to Machine Perception

Aug. 25 Lecture 2 Linear Models

Aug. 30 Lecture 3 Optimization

Sept. 1 Lecture 4 Deep-Nets and Backpropagation

Sept. 1 Deadline Presentation Preference Due

Select from the following:
Sept. 6 Lecture 5 How to Read and Review Papers

--- Topic Perception

Select from the following:
Sept. 8 Lecture 6 Convolution Neural Networks-I

Sept. 13 Lecture 7 Convolution Neural Networks-II

Sept. 15 Discussion 1 Student Presentation
Sept. 20 Lecture 8 Object Detection & Segmentation

Sept. 22 Discussion 2 Student Presentation
Sept. 27 Lecture 9 Recurrent Neural Network

Sept. 29 Discussion 3 Student Presentation
Oct. 4 Lecture 10 Graph Neural Network

Oct. 6 Discussion 4 Student Presentation
Oct. 11 --- October Break

Select from the following:
Oct. 13 Lecture 11 Attention and Transformers

Oct. 18 Lecture 12 Vision Transformers

Oct. 20 Discussion 5 Student Presentation
Oct. 20 Deadline Project proposal due

Select from the following:
Oct. 20 Deadline Homework (Review 1) Due

Select from the following:
--- Topic Generation

Select from the following:
Oct. 25 Lecture 13 Variational Auto-Encoders

Oct. 27 Discussion 6 Student Presentation
Nov. 1 Lecture 14 Diffusion Models

Nov. 3 Discussion 7 Student Presentation
Nov. 8 Lecture 15 Generative Adversarial Networks

Nov. 10 Discussion 8 Student Presentation
--- Topic Other Topics

Select from the following:
Nov. 15 Lecture 16 Self-Supervised Learning

Nov. 17 Discussion 9 Student Presentation
Nov. 22 Lecture 17 Language and Vision

Nov. 24 --- Thanksgiving Break

Select from the following:
Nov. 29 Discussion 10 Student Presentation
Dec. 1 Lecture 18 Audio and Vision

Dec. 6 Discussion 11 Student Presentation
Dec. 6 Deadline Homework (Review 2) Due

Select from the following:
Dec. 8 Lecture 19 Final Project Spotlights

Dec. 8 Lecture 20 Final Project Report Due


Policies

Late Policy

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University Policies

Please refer to additional university policies in BrightSpace.