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start [2021/12/02 09:20]
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start [2022/03/10 13:55] (current)
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-====== Schedule ====== +The schedule for Fall 2022 is being developed and will be available soon.
-/*** +
- +
-Links to MS Teams Events: +
-  - Lectures: [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598126507312?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Tuesdays/Thursdays, 12:30 - 1:45 PM]]. +
-  - Office Hours with Chuck: [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598288422204?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Wednesdays, 10:00 - 11:00 AM]] +
-  - Office Hours with Dejan: [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598541704203?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Mondays, 1:00 - 3:00 PM]] and [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598541786577?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Wednesdays, 3:00 - 5:00]] +
- +
- +
-Lecture videos are available from the [[https://colostate.instructure.com/courses/109894|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 tentative schedule will be updated during the semester. +
- +
- +
-===== August ===== +
- +
-|< 100% 18% 20% 22% 20% 20%  >| +
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments +
-| Week 1:\\  Aug 24, 26   | Overview of course. Review of neural networks training and use.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/01 Introduction to CS545.ipynb|01 Introduction to CS545]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/02 Searching for Good Weights in a Linear Model.ipynb|02 Searching for Good Weights in a Linear Model]]    | <color red>Ungraded</color> [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Quiz1.ipynb|Quiz 1]] due Friday, August 27, 10:00 PM  | +
-| Week 2:\\  Aug 31, Sept 2  | Regression with neural networks.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/03 Fitting Simple Models Using Gradient Descent in the Squared Error.ipynb|03 Fitting Simple Models Using Gradient Descent in the Squared Error]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/04 Introduction to Neural Networks.ipynb|04 Introduction to Neural Networks]] +
- +
- +
-===== September ===== +
- +
-|< 100% 18% 20% 22% 20% 20%  >| +
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments +
-| Week 3:\\  Sept 7, 9  | A1 questions. Optimizers. Neural Network class.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/05 Optimizers.ipynb|05 Optimizers]]  | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A1 Three-Layer Neural Network.ipynb|A1 Three-Layer Neural Network]] due Wednesday, Sept 8th, at 10:00 PM  | +
-| Week 4:\\  Sept 14, 16  | A2. Autoencoders. Classification.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/06 Autoencoders.ipynb|06 Autoencoders]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/07 Introduction to Classification.ipynb|07 Introduction to Classification]]  | |  | +
-| Week 5:\\  Sept 21, 23  | A2 questions. Classification.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/08 Classification with Linear Logistic Regression.ipynb|08 Classification with Linear Logistic Regression]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/09 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|09 Classification with Nonlinear Logistic Regression Using Neural Networks]]  |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A2 NeuralNetwork Class.ipynb|A2 NeuralNetwork Class]] due <color red>Tuesday, Sept. 21, at 10:00 PM </color>. Examples of good solutions are [[https://www.cs.colostate.edu/~anderson/cs545/goodSolutions|available here.]]   | +
-| Week 6:\\  Sept 28, 30  | Lectures and Chuck's office hours <color red>canceled</color> this week. Prerecorded lectures available in Echo360 in Canvas on Jax and Streamlit packages.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/10 JAX.ipynb|10 JAX]]\\ [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/neuralnetworks_app.tar|neuralnetworks_app.tar]]  | [[https://moocaholic.medium.com/jax-a13e83f49897|JAX Ecosystem]]\\ [[https://streamlit.io/|Streamlit]]  | |   +
- +
-===== October ===== +
- +
-|< 100% 18% 20% 22% 20% 20%  >| +
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments +
-| Week 7:\\  Oct 5, 7  | Convolutional neural networks.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/11 Convolutional Neural Networks.ipynb|11 Convolutional Neural Networks]]\\ [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/CNN Backprop.pdf|CNN Backpropagation Notes]]  | [[https://spectrum.ieee.org/special-reports/the-great-ai-reckoning/|The Great AI Reckoning]]  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A3 Neural Network Classifier.ipynb|A3 Neural Network Classifier]] due Friday, Oct 8, at 10:00 PM. Examples of good solutions are [[https://www.cs.colostate.edu/~anderson/cs545/goodSolutions|available here.]] | +
-| Week 8:\\  Oct 12, 14  | Pytorch. Convolutional neural nets  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 Introduction to Pytorch.ipynb|12 Introduction to Pytorch]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13 Convolutional Neural Networks in Pytorch.ipynb|13 Convolutional Neural Networks in Pytorch]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/14 Convolutional Neural Networks in Numpy.ipynb|14 Convolutional Neural Networks in Numpy]] +
-| Week 9:\\  Oct 19, 21  | Reinforcement Learning  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/15 Introduction to Reinforcement Learning.ipynb|15 Introduction to Reinforcement Learning]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/16 Reinforcement Learning with Neural Network as Q Function.ipynb|16 Reinforcement Learning with Neural Network as Q Function]]  | |   | +
-| Week 10:\\  Oct 26, 28  | Reinforcement Learning\\ Oct 28 in-person lecture and office hours are <color red>canceled.</color> Please watch the recorded lecture available in Canvas Echo360 available by Oct 29. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/17 Reinforcement Learning for Two Player Games.ipynb|17 Reinforcement Learning for Two Player Games]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/18 Reinforcement Learning to Control a Marble.ipynb|18 Reinforcement Learning to Control a Marble]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/19 Reinforcement Learning Modular Framework.ipynb|19 Reinforcement Learning Modular Framework]] |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A4.2 Convolutional Neural Networks.ipynb|A4.2 Convolutional Neural Networks]] due Monday, Oct 25, at 10:00 PM.  | +
- +
-===== November ===== +
- +
-|< 100% 18% 20% 22% 20% 20%  >| +
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments +
-| Week 11:\\  Nov 2, 4  | Transfer learning in Reinforcement Learning.\\ Brain-Computer Interfaces  | Slide presentations  | [[http://www.cs.colostate.edu/~anderson/wp/pubs/pretrainijcnn15.pdf|Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics]], [[https://ieeexplore.ieee.org/document/9533751|Increased Reinforcement Learning Performance through Transfer of Representation Learned by State Prediction Model]]  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Project Proposal and Report Example.ipynb|Project Proposal and Report Example]] due Friday, Nov. 5, at 10:00 PM.  |  +
-| Week 12:\\  Nov 9, 11  | BCI. Recurrent Neural Networks. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/20 Recurrent Networks in Numpy.ipynb|20 Recurrent Networks in Numpy]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/21 Recurrent Networks in Pytorch.ipynb|21 Recurrent Networks in Pytorch]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/22 Classifying EEG Using Recurrent Neural Networks.ipynb|22 Classifying EEG Using Recurrent Neural Networks]]  | |  | +
-| Week 13:\\  Nov 16, 18  | K-means clustering. K-nearest-neighbor classification. Support Vector Machines.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/23 K-Means Clustering, K-Nearest-Neighbor Classification.ipynb|23 K-Means Clustering, K-Nearest-Neighbor Classification]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/24 Support Vector Machines.ipynb|24 Support Vector Machines]]    |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A5.1 Reinforcement Learning on Marble with Variable Goal.ipynb|A5.1 Reinforcement Learning on Marble with Variable Goal]] due Monday, Nov 15th at 10:00 PM. <color red>A5grader.tar updated Nov 11, 2:00 PM</color>. The text showing the last test has been corrected. +
-| Nov 23, 25  |  Fall Recess !!  | +
-| Week 14:\\  Nov 30, Dec 2  | Introduction to Transformers  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/25 Introduction to Transformers.ipynb|25 Introduction to Transformers]] |  +
- +
-===== December ===== +
- +
-|< 100% 18% 20% 22% 20% 20%  >| +
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments +
-| Week 15:\\  Dec 7, 9  |   | +
-| Dec 13-17  |  Final Exam Week  |  No Exams in this course  | | Final project reports are due Monday, Dec. 13th, 10:00 PM | +
  
start.txt · Last modified: 2022/03/10 13:55 (external edit)