Lectures

I would like to thank Prof. Alexander Schwing, Prof. Aigou Han, Prof. Farzad Kamalabadi, and Prof. Corey Synder for their support and lecture documents which I’ve based much of my own work on.

Also, quick hint, icons are links to slides and video contents! Also if the video link hasn’t appeared on the website, you can check the mediaspace channel directly!

#	Title	Date
0	Lecture 0 - PyTorch Intro, tensors, views, indexing	Jan 21
1	Lecture 1 - PyTorch storage, indexing, cpu/gpu data types, functions	Jan 23
2	Lecture 2 - Linear algebra I	Jan 28
3	Lecture 3 - Linear algebra II & Matrix algebra	Jan 30
4	Lecture 4 - Matrix calculus II and computation graphs	Feb 04
5	Lecture 5 - Gradient descent	Feb 06
6	Lecture 6 - Primal optimization	Feb 11
7	Lecture 7 - Linear regression I	Feb 13
8	Lecture 8 - Linear regression II	Feb 18
9	Lecture 9 - Logistic regression	Feb 20
10	Lecture 10 - Multi-class logistic regression	Feb 25
11	Lecture 11 - PyTorch optimizers, datasets, dataloaders I	Feb 27
12	Lecture 12 - PyTorch optimizers, datasets, dataloaders II	Mar 04
13	Lecture 13 - Review for MT1	Mar 06
14	Lecture 14 - Deep Nets I - Simple, multi-layer networks	Mar 25
15	Lecture 15 - Deep Nets II - Convolutional networks	Mar 27
16	Lecture 16 - Deep Nets III - Recurrent networks	Apr 01
17	Lecture 17 - Principal component analysis	Apr 03
18	Lecture 18 - K-means clustering, gaussian mixture models	Apr 08
19	Lecture 19 - Generative adversarial networks	Apr 10
20	Lecture 20 - Object detection, semantic segmentation	Apr 15
21	Lecture 21 - Words and attention	Apr 17
22	Lecture 22 - Transformer models	Apr 22
23	Lecture 23 - Large language models I - architectures and training	Apr 24
24	Lecture 24 - Large language models II - fine-tuning methods	Apr 29
25	Lecture 25 - Course recap and celebration	May 06