schedule
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Schedule
Links to MS Teams Events:
- Lectures: Tuesdays/Thursdays, 12:30 - 1:45 PM.
- Office Hours with Chuck: Wednesdays, 10:00 - 11:00 AM
- Office Hours with Dejan: Mondays, 1:00 - 3:00 PM and Wednesdays, 3:00 - 5:00
Lecture videos are available from the Canvas home page.
To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:
export PATH=/usr/local/anaconda/bin:$PATH
This is a tentative schedule of CS545 topics for Fall, 2020. This will be updated during the summer and as the fall semester continues.
August
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 1: Aug 24 - Aug 28 | Overview of course and the machine learning field. Reminder of how python is used in machine learning. | 01 Introduction to CS545 02 Searching for Good Weights in a Linear Model | From Python to Numpy, Chapters 1 - 2 Scipy Lectures, Section 1 Visualization with Matplotlib Deep Learning, Chapters 1 - 5.1.4 |
September
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 2: Aug 31 - Sept 4 | Help with A1. Review of gradients. Gradient descent with SGD, Adam and SCG, | 03 Fitting Simple Models Using Gradient Descent in the Squared Error | A1.4 Polynomial Model due Friday, Sept 4th, at 10:00 PM | |
Week 3: Sept 7 - Sept 11 | Implementing neural networks with numpy to predict real-valued variables. Deriving gradients. | 04 Scaled Conjugate Gradient 05 Introduction to Gradient Descent for Neural Networks | ||
Week 4: Sept 14 - Sept 18 | Error gradients for neural networks as matrix equations. Discussion of A2. Introduction to dashboards with python using streamlit. | 06 Introduction to Streamlit | streamlit.io | |
Week 5: Sept 21 - 25 | Use of Optimizers for neural networks. Introduction to Pytorch and automatic differentation. | 07 Collect Weights in Vector for Optimizers 08 Pytorch autograd, nn.Module | A2.2 Multilayer Neural Network due Friday, Sept 25th, at 10:00 PM. Good examples of solutions are available here. |
October
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 6: Sept 28 - Oct 2 | Neural Network class. | 09 Initial Steps towards Defining a NeuralNetwork Class | ||
Week 7: Oct 5 - Oct 9 Oct 8 Lecture will not meet, but recording will be available. | Help with A3. Dimensionality reduction. | 10 Help with A3 11 Low-Dimensional Representations of Data | A3.3 Neural Network Class due Monday, Oct 12, 10:00 PM Examples of good solutions are available here. |
|
Week 8: Oct 12 - Oct 16 | Brief overview of notes 11. Introduction to Classification | 12 Classification with Neural Networks | ||
Week 9: Oct 19 - Oct 23 | Convolutional neural networks in numpy. | 13 NeuralNetwork_Pytorch 14 Introduction to Convolution | ||
Week 10: Oct 26 - Oct 30 | Fully-connected and Convolutional Neural Nets in Pytorch | 15 Convolutional Neural Networks 16.1 Convolutional Neural Networks in Pytorch | A4.1 Neural Network Classifier due Tuesday Oct 27, at 10:00 PM Good examples of solutions are available here. |
November
Week | Topic | Material | Reading | Assignments | |
---|---|---|---|---|---|
Week 11: Nov 2 - Nov 6 | Comparing network performance. Introduction to Reinforcement Learning. Deep Reinforcement Learning | 17 Partitioning Data to Compare Neural Network Performance 18 Introduction to Reinforcement Learning 19 Reinforcement Learning with Neural Network as Q Function | Reinforcement Learning: An Introduction, by Richard Sutton and Andrew Barto, 2nd edition | ||
Week 12: November 9 - 13 | Deep reinforcement learning on simulated physical control problem. | 20 Reinforcement Learning to Control a Marble | |||
Week 13: Nov 16 - Nov 20 | A5 Neural Networks in Pytorch due Wednesday, Nov 18 at 10:00 PM Good examples of solutions are available here. | ||||
Nov 23 - Nov 27 | Fall Recess! |
December
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 14: Nov 30 - Dec 4 | Clustering. Support Vector Machines. | 22 K-Means Clustering, K-Nearest-Neighbor Classification 23 Support Vector Machines | ||
Week 15: Dec 7 - Dec 11 | Transfer learning in Reinforcement Learning. Brain-computer interfaces. | |||
Finals Week: Dec 14 - Dec 18 | A6.2 Reinforcement Learning to Control a Robot due Tuesday, Dec 15th, 10:00 PM. Here is A3mysolution.tar, a neural network implementation you may choose to use for A6. Good examples of solutions are available here. |
schedule.1608415008.txt.gz · Last modified: 2020/12/19 14:56 (external edit)