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This is an old revision of the document!

The following schedule is tentative and is being updated.

Please send your suggestions regarding lecture topics to Chuck using this Google Docs form. Questions regarding assignments should be entered in Canvas discussions.


Week Topic Material Reading Assignments
Week 1:
Aug 23, 256
Overview of course. Review of neural networks training and use. 01 Introduction to CS545
02 Searching for Good Weights in a Linear Model
JupyterLab Introduction, watch the video then play with jupyter lab.
The Batch from DeepLearning.AI. Yay, Colorado!
Week 2:
Aug 30, Sept 1
Thursday lecture cancelled. Please watch pre-recorded lecture in Echo360. Quiz1 and A1 questions. Regression with neural networks. 03 Fitting Simple Models Using Gradient Descent in the Squared Error Quiz 1 due Wednesday, August 31, 10:00 PM, in Canvas




Week Topic Material Reading Assignments
Week 11:
Nov 1, 3
Transfer learning in Reinforcement Learning.
Brain-Computer Interfaces
Slide presentations
Week 12:
Nov 8, 10
BCI. Recurrent Neural Networks. 20 Recurrent Networks in Numpy
21 Recurrent Networks in Pytorch
22 Classifying EEG Using Recurrent Neural Networks
Week 13:
Nov 15, 17
K-means clustering. K-nearest-neighbor classification. Support Vector Machines. 23 K-Means Clustering, K-Nearest-Neighbor Classification
24 Support Vector Machines
Week 14:
Nov 29, Dec 1
Introduction to Transformers 25 Introduction to Transformers


Week Topic Material Reading Assignments
Week 15:
Dec 6, 8
Transformers: Self-Attention Replaced by Fourier Transform.
Cascade Ensemble Network
26 FNet--Replace Self-Attention with Fourier Transform
27 Cascade Ensemble Network
Dec 12-16 Final Exam Week No Exams in this course
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