schedule
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.
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 | Optimizers. Neural Network class. | 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 Chuck's office hours on the 13th are canceled. | Optimizers. Python classes. A2. | 05 Optimizers | A1 Three-Layer Neural Network due Monday, Sept 12th, at 10:00 PM A1grader.tar updated Sept. 7th 3:30 pm |
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Week 5: Sept 20, 22 | Autoencoders. Classification. | 06 Autoencoders 07 Introduction to Classification 08 Classification with Linear Logistic Regression 09 Classification with Nonlinear Logistic Regression Using Neural Networks | ||
Week 6: Sept 27, 29 | 10 JAX neuralnetworks_app.tar | JAX Ecosystem Streamlit |
October
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 7: Oct 4, 6 | Convolutional neural networks. | 11 Convolutional Neural Networks CNN Backpropagation Notes | ||
Week 8: Oct 11, 13 | Pytorch. Convolutional neural nets | 12 Introduction to Pytorch 13 Convolutional Neural Networks in Pytorch 14 Convolutional Neural Networks in Numpy | ||
Week 9: Oct 18, 20 | Reinforcement Learning | 15 Introduction to Reinforcement Learning 16 Reinforcement Learning with Neural Network as Q Function | ||
Week 10: Oct 25, 27 | Reinforcement Learning | 17 Reinforcement Learning for Two Player Games 18 Reinforcement Learning to Control a Marble 19 Reinforcement Learning Modular Framework |
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 |
schedule.1662752643.txt.gz · Last modified: 2022/09/09 13:44 by anderson