Image Classification

Convolutional neural network (CNN)

BERT

Transformer (machine learning model)

Machine learning

Network Architectures

Optimizers

Neural network

Segmentation and Detection

Deep Reinforcement Learning

Optimization

Sequential Data

Generative adversarial network (GAN)

CSE 490 G1 / 599 G1 Introduction to Deep Learning University of Washington Autumn 2019 A survey course on neural network implementation and applications, including image processing, classification, detection, and segmentation. The course also covers semantic understanding, translation, and question-answering applications. It's ideal for those with a background in Machine Learning, Neural Networks, Optimization, and CNNs. A survey class of neural network implementation and applications. Topics include: optimization - stochastic gradient descent, adaptive and 2nd order methods, normalization; convolutional neural networks - image processing, classification, detection, segmentation; recurrent neural networks - semantic understanding, translation, question-answering; cross-domain applications - image captioning, vision and language.   

CSE 490 G1 / 599 G1 Introduction to Deep Learning

Deep Learning

Image Filtering

Image Pyramids

Frequency Domain

Hough Transform

Detecting Corners

Feature Detectors and Descriptors

2D Transformations

Image Homographies

Geometric Camera Models

Two-View Geometry

Stereo

Optical Flow

Alignment and Tracking

Radiometry and Reflectance

Photometric Stereo

Digital Photography

16-385 Computer Vision Carnegie Mellon University Spring 2022 This course gives an expansive introduction to computer vision, focusing on image processing, recognition, geometry-based and physics-based vision, and video analysis. Students will gain practical experience solving real-life vision problems. It requires a good understanding of linear algebra, calculus, and programming. This course provides a comprehensive introduction to computer vision. Major topics include image processing, detection and recognition, geometry-based and physics-based vision and video analysis. Students will learn basic concepts of computer vision as well as hands on experience to solve real-life vision problems.  This course requires familiarity with linear algebra, calculus, basic probability, as well as programming. In particular, the following courses serve as prerequisite:

- "Mathematical Foundations of Electrical Engineering" (18-202) and "Principles of Imperative Computation" (15-122)

OR

- "Matrix Algebra with Applications" (21-240) and "Matrices and Linear Transformations" (21-241) and "Calculus in Three Dimensions" (21-259) and "Principles of Imperative Computation" (15-122) Readings will be assigned from the following textbook (available online for free):

- **Computer Vision**: Algorithms and Applications, by Richard Szeliski.
Additional readings will be assigned from relevant papers. Readings will be posted on the website.

The following textbooks can also be useful references for different parts of the class, but are not required:

- Multiple View Geometry in Computer Vision, by Richard Hartley and Andrew Zisserman.
- Computer Vision: A Modern Approach, by David Forsyth and Jean Ponce.
- Digital Image Processing, by Rafael Gonzalez and Richard Woods.

16-385 Computer Vision

Computer Vision

Image Classification

2 courses cover this concept

CSE 490 G1 / 599 G1 Introduction to Deep Learning

16-385 Computer Vision