Lecture 1: Introduction to Deep Learning for Computer Vision (UMich EECS 498-007)

Lecture 2: Image Classification (UMich EECS 498-007)

Lecture 3: Linear Classifiers (UMich EECS 498-007)

Lecture 4: Optimization (UMich EECS 498-007)

Lecture 5: Neural Networks (UMich EECS 498-007)

Lecture 6 Backpropagation (UMich EECS 498-007)

Lecture 7: Convolutional Networks (UMich EECS 498-007)

Lecture 8: CNN Architectures (UMich EECS 498-007)

Lecture 9: Hardware and Software (UMich EECS 498-007)

Lecture 10: Training Neural Networks Part 1 (UMich EECS 498-007)

Lecture 11: Training Neural Networks Part 2 (UMich EECS 498-007)

Lecture 12: Recurrent Neural Networks (UMich EECS 498-007)

Lecture 13: Attention (UMich EECS 498-007)

Guest Lecture: Adversarial Machine Learning (UMich EECS 498-007)

Lecture 14: Visualizing and Understanding (UMich EECS 498-007)

Lecture 15: Object Detection (UMich EECS 498-007)

Lecture 16: Detection and Segmentation (UMich EECS 498-007)

Lecture 17: 3D Vision (UMich EECS 498-007)

Lecture 18: Videos (UMich EECS 498-007)

Lecture 19: Generative Models Part 1 (UMich EECS 498-007)

Lecture 20: Generative Models Part 2 (UMich EECS 498-007)

Lecture 21: Reinforcement Learning (UMich EECS 498-007)

Lecture 22: Recap & Open Problems (UMich EECS 498-007)