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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.

August

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

September

Week Topic Material Reading Assignments
Week 3:
Sept 6, 8
Introduction to Neural Networks 04 Introduction to Neural Networks
04a Simple Animations
Activation functions in deep learning: A comprehensive survey and benchmark, Neurocomputing, volume 503, 2022, pp. 92-108
Week 4:
Sept 13, 15
Python classes. A2. 04b Introduction to Python Classes Classes Tutorial A1 Three-Layer Neural Network due Monday, Sept 12th, at 10:00 PM
Anderson-Solution-A1
Week 5:
Sept 20, 22
Optimizers. Autoencoders. 05 Optimizers Updated Sept. 27th
05a Collecting All Weights into One-Dimensional Vector for Use in Optimizers
06 Autoencoders
06a Visualizing Weights New
Pandas Cheat Sheet A2 NeuralNetwork Class due Thursday, Sept 22nd, at 10:00 PM
Anderson-A2-Solution
Week 6:
Sept 27, 29
A3. Classification 07 Introduction to Classification JAX Ecosystem
Streamlit

October

November

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

December

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.txt · Last modified: 2022/10/04 10:36 by 127.0.0.1