Grad-CP8309-CMF-F2018 Deep Learning in Computer Vision

with Prof. Kosta Derpanis (Ryerson University)

Course description. Computer Vision is broadly defined as the study of recovering useful properties of the world from one or more images.  In recent years, Deep Learning has emerged as a powerful tool for addressing computer vision tasks.  This course will cover a range of foundational topics at the intersection of Deep Learning and Computer Vision.

Contact information.
kosta[at]scs.ryerson[dot]ca

Homepage
www.scs.ryerson.ca/~kosta

Prerequisites. University-level courses on linear algebra, multivariable calculus, and probability

Required textbook. Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning (available for free or purchase)

Optional textbook.  Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong, Mathematics for Machine Learning, (preprint available for free)

Lectures.

# TOPIC SLIDES
PDF    MOV
0
Introduction to Computer Vision PDF MOV
1
Machine Learning Crash Course
PDF MOV
2
Ethics, Privacy and Security in Machine Learning
PDF MOV
NEURAL NETWORK FOUNDATIONS
3
Multilayer Perceptron
PDF MOV
4
Convolutional Network (ConvNet)
PDF MOV
5
Backprop
PDF MOV
6
Optimization
PDF MOV
7
Introduction to PyTorch (version 1.0)
PDF MOV
SPATIAL MODELS
8
Object Recognition Architectures PDF MOV
9
Training Networks
PDF MOV
10
Weight Initialization
PDF MOV
11
Transfer Learning PDF MOV
12
Object Detection PDF MOV
13
Metric Learning
PDF MOV
14
Pixel Labeling Tasks PDF MOV
15
Optical Flow
PDF MOV
16
Segmentation Aware Filtering
PDF MOV
VISUALIZATION
17
Understanding ConvNets
PDF MOV
18
Texture Synthesis
PDF MOV
19
Style Transfer
PDF MOV
SEQUENTIAL MODELS
20
Recurrent Neural Network (RNN)
PDF MOV
21
Long Short-Term Memory (LSTM)
PDF MOV
22
Bidirectional RNN
PDF MOV
23
Vision and Language
PDF MOV
24
Action Recognition
PDF MOV
SELF-SUPERVISED LEARNING
25
Look ma no labels: Learning without labels
PDF MOV
GENERATIVE MODELS
26
PixelNN (PixelRNN and PixelCNN)
PDF MOV
27
Variational Autoencoder (VAE)
28
Invertible Density Models - Normalizing Flows
29
Generative Adversarial Network (GAN)
ADVERSARIAL EXAMPLES
30
Adversarial Examples
PDF MOV

Related courses.
     Ryerson University CPS843: Introduction to Computer Vision with Kosta Derpanis
     Stanford CS231N: Convolutional Neural Networks for Visual Recognition with Fei Fei Li, Justin Johnson and Serena Young
     EPFL EE-559: Deep Learning
with Francois Fleuret
 

Acknowledgements. While a great effort has been made to assemble an original set of lecture slides, the essence of the presentation of many of the slides rely on material prepared by the following people: Andrej Karpathy, Justin Johnson, Serena Young, Fei Fei Li, Francois Fleuret, Graham Taylor, Carl Doersch