Prof. Vineeth N B
Professor , Computer science and Engineering , IIT Hyderabad
Degree Level Course
Deep Learning for Computer Vision
-Knowledge of basics of image processing and computer vision -Knowledge of building blocks of deep learning including feedforward networks, convolutional neural networks, recurrent neural networks and transformers -Knowledge of generative AI models in computer vision -Knowledge of recent trends including explainability/zero-shot learning, few-shot learning, self-supervised learning, etc -Hands-on experience on implementation of basic image processing tasks -Hands-on experience on implementation of deep learning models for computer vision tasks -Hands-on experience on implementation of advanced computer vision tasks such as explainability, self-supervised learning, etc
Taught by
Prof. Vineeth N B
Code
BSDA5006
Credits
4 Credits
Type
Elective
Prerequisites
None
12-Week Roadmap
Course Structure & Syllabus
For details of standard term assessment timelines and exam structures, visit our Academics page.
WEEK 1
Introduction and Overview:
Course Overview and Motivation; Introduction to Image Formation, Capture and
Representation; Linear Filtering, Correlation, Convolution
WEEK 2
Visual Features and Representations:
Edge, Blobs, Corner Detection; Scale Space and Scale Selection; SIFT, SURF; HoG,LBP, etc.
WEEK 3
Visual Matching:
Bag-of-words, VLAD; RANSAC, Hough transform; Pyramid Matching; Optical Flow
WEEK 4
Deep Learning Review:
Review of Deep Learning, Multi-layer Perceptrons, Backpropagation
Reading List
Prescribed Books & References
- Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, 2016
- Michael Nielsen, Neural Networks and Deep Learning, 2016
- Yoshua Bengio, Learning Deep Architectures for AI, 2009
- Richard Szeliski, Computer Vision: Algorithms and Applications, 2010.
- Simon Prince, Computer Vision: Models, Learning, and Inference, 2012.
- David Forsyth, Jean Ponce, Computer Vision: A Modern Approach, 2002.
Faculty & Experts
About the Instructors