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The following schedule is tentative and is being updated.


Week Topic Material Reading Assignments
Week 1:
Aug 23, 25
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. Quiz 1 due Friday, August 26, 10:00 PM
Week 2:
Aug 30, Sept 1
Regression with neural networks. 03 Fitting Simple Models Using Gradient Descent in the Squared Error
04 Introduction to Neural Networks


Week Topic Material Reading Assignments
Week 3:
Sept 6, 8
A1 questions. Optimizers. Neural Network class. 05 Optimizers
Week 4:
Sept 13, 15
A2. Autoencoders. Classification. 06 Autoencoders
07 Introduction to Classification
Week 5:
Sept 20, 22
Classification. 08 Classification with Linear Logistic Regression
09 Classification with Nonlinear Logistic Regression Using Neural Networks
Week 6:
Sept 27, 29
10 JAX
JAX Ecosystem



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
start.1660258247.txt.gz · Last modified: 2022/08/11 16:50 by anderson