CS59200-TMP: Topics in Machine Perception (Fall 2022)
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:
- Probability
- Linear algebra
- Proficiency in Python
Textbooks (Optional):
- [Murphy2022a] Murphy, K.
Probabilistic Machine Learning: An Introduction.
(2022) [Available] - [Murphy2022b] Murphy, K.
Probabilistic Machine Learning: Advanced Topics.
(2022) [Available] - [Szeliski2022] Szeliski, R.
Computer Vision: Algorithms and Applications, 2nd ed.
(2022) [Available] - [Zhang2021] Zhang, Aston, et al.
Dive into deep learning.
(2021) [Available]
Grading:
- Homework: 30%
- Presentation: 35%
- Final Project: 35%
Instructor
Raymond A. Yeh
Time & Location
- Time: Tuesday, Thursday 10:30AM-11:45AM
- Location: LWSN B134
- Office Hour: By appointment
Other Materials
Course Schedule
Date | Type | Description | Materials |
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--- | Topic | Basics
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Aug. 23 | Lecture 1 | Intro to Machine Perception
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Aug. 25 | Lecture 2 | Linear Models
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Aug. 30 | Lecture 3 | Optimization
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Sept. 1 | Lecture 4 | Deep-Nets and Backpropagation
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Sept. 1 | Deadline | Presentation Preference Due
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Sept. 6 | Lecture 5 | How to Read and Review Papers
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--- | Topic | Perception
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Sept. 8 | Lecture 6 | Convolution Neural Networks-I
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Sept. 13 | Lecture 7 | Convolution Neural Networks-II
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Sept. 15 | Discussion 1 | Student Presentation | [Expand] |
Sept. 20 | Lecture 8 | Object Detection & Segmentation
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Sept. 22 | Discussion 2 | Student Presentation
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[Expand] |
Sept. 27 | Lecture 9 | Recurrent Neural Network
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Sept. 29 | Discussion 3 | Student Presentation
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[Expand] |
Oct. 4 | Lecture 10 | Graph Neural Network
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Oct. 6 | Discussion 4 | Student Presentation | [Expand] |
Oct. 11 | --- | October Break
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Oct. 13 | Lecture 11 | Attention and Transformers
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Oct. 18 | Lecture 12 | Vision Transformers
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Oct. 20 | Discussion 5 | Student Presentation | [Expand] |
Oct. 20 | Deadline | Project proposal due
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Oct. 20 | Deadline | Homework (Review 1) Due
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--- | Topic | Generation
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Oct. 25 | Lecture 13 | Variational Auto-Encoders
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Oct. 27 | Discussion 6 | Student Presentation | [Expand] |
Nov. 1 | Lecture 14 | Diffusion Models
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Nov. 3 | Discussion 7 | Student Presentation | [Expand] |
Nov. 8 | Lecture 15 | Generative Adversarial Networks
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Nov. 10 | Discussion 8 | Student Presentation
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[Expand] |
--- | Topic | Other Topics
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Nov. 15 | Lecture 16 | Self-Supervised Learning
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Nov. 17 | Discussion 9 | Student Presentation
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[Expand] |
Nov. 22 | Lecture 17 | Language and Vision
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Nov. 24 | --- | Thanksgiving Break
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Nov. 29 | Discussion 10 | Student Presentation | [Expand] |
Dec. 1 | Lecture 18 | Audio and Vision
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Dec. 6 | Discussion 11 | Student Presentation | [Expand] |
Dec. 6 | Deadline | Homework (Review 2) Due
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Dec. 8 | Lecture 19 | Final Project Spotlights
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Dec. 8 | Lecture 20 | Final Project Report Due
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