Lecture 1 - Course Introduction
Lecture 2 - History
Lecture 3 - Image Formation
Lecture 4 - Image Representation
Lecture 5 - Linear Filtering
Lecture 6 - Image in Frequency Domain
Lecture 7 - Image Sampling
Lecture 8 - Edge Detection
Lecture 9 - From Edges to Blobs and Corners
Lecture 10 - Scale Space, Image Pyramids and Filter Banks
Lecture 11 - Feature Detectors: SIFT and Variants
Lecture 12 - Image Segmentation
Lecture 13 - Other Feature Spaces
Lecture 14 - Human Visual System
Lecture 15 - Feature Matching
Lecture 16 - Hough Transform
Lecture 17 - From Points to Images: Bag-of-Words and VLAD Representations
Lecture 18 - Image Descriptor Matching
Lecture 19 - Pyramid Matching
Lecture 20 - From Traditional Vision to Deep Learning
Lecture 21 - Neural Networks: A Review - Part 1
Lecture 22 - Neural Networks: A Review - Part 2
Lecture 23 - Feedforward Neural Networks and Backpropagation - Part 1
Lecture 24 - Feedforward Neural Networks and Backpropagation - Part 2
Lecture 25 - Gradient Descent and Variants - Part 1
Lecture 26 - Gradient Descent and Variants - Part 2
Lecture 27 - Regularization in Neural Networks - Part 1
Lecture 28 - Regularization in Neural Networks - Part 2
Lecture 29 - Improving Training of Neural Networks - Part 1
Lecture 30 - Improving Training of Neural Networks - Part 2
Lecture 31 - Convolutional Neural Networks: An Introduction - Part 1
Lecture 32 - Convolutional Neural Networks: An Introduction - Part 2
Lecture 33 - Backpropagation in CNNs
Lecture 34 - Evolution of CNN Architectures for Image Classification - Part 1
Lecture 35 - Evolution of CNN Architectures for Image Classification - Part 2
Lecture 36 - Recent CNN Architectures
Lecture 37 - Finetuning in CNNs
Lecture 38 - Explaining CNNs: Visualization Methods
Lecture 39 - Explaining CNNs: Early Methods
Lecture 40 - Explaining CNNs: Class Attribution Map Methods
Lecture 41 - Explaining CNNs: Recent Methods - Part 1
Lecture 42 - Explaining CNNs: Recent Methods - Part 2
Lecture 43 - Going Beyond Explaining CNNs
Lecture 44 - CNNs for Object Detection-I - Part 1
Lecture 45 - CNNs for Object Detection-I - Part 2
Lecture 46 - CNNs for Object Detection-II
Lecture 47 - CNNs for Segmentation
Lecture 48 - CNNs for Human Understanding: Faces - Part 1
Lecture 49 - CNNs for Human Understanding: Faces - Part 2
Lecture 50 - CNNs for Human Understanding: Human Pose and Crowd
Lecture 51 - CNNs for Other Image Tasks
Lecture 52 - Recurrent Neural Networks: Introduction
Lecture 53 - Backpropagation in RNNs
Lecture 54 - LSTMs and GRUs
Lecture 55 - Video Understanding using CNNs and RNNs
Lecture 56 - Attention in Vision Models: An Introduction
Lecture 57 - Vision and Language: Image Captioning
Lecture 58 - Beyond Captioning: Visual QA, Visual Dialog
Lecture 59 - Other Attention Models
Lecture 60 - Self-Attention and Transformers
Lecture 61 - Deep Generative Models: An Introduction
Lecture 62 - Generative Adversarial Networks - Part 1
Lecture 63 - Generative Adversarial Networks - Part 2
Lecture 64 - Variational Autoencoders
Lecture 65 - Combining VAEs and GANs
Lecture 66 - Beyond VAEs and GANs: Other Deep Generative Models - Part 1
Lecture 67 - Beyond VAEs and GANs: Other Deep Generative Models - Part 2
Lecture 68 - GAN Improvements
Lecture 69 - Deep Generative Models across Multiple Domains
Lecture 70 - VAEs and DIsentanglement
Lecture 71 - Deep Generative Models: Image Applications
Lecture 72 - Deep Generative Models: Video Applications
Lecture 73 - Few-shot and Zero-shot Learning - Part 1
Lecture 74 - Few-shot and Zero-shot Learning - Part 2
Lecture 75 - Self-Supervised Learning
Lecture 76 - Adversarial Robustness
Lecture 77 - Pruning and Model Compression
Lecture 78 - Neural Architecture Search
Lecture 79 - Course Conclusion
