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--- start [2022/10/11 21:22] – external edit 127.0.0.1
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 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
 | Week 7:\\  Oct 4, 6  | Classification. Convolutional neural networks.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/08 Classification with Linear Logistic Regression.ipynb|08 Classification with Linear Logistic Regression]]\\ [[https://nbviewer.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]]  | [[https://spectrum.ieee.org/special-reports/the-great-ai-reckoning/|The Great AI Reckoning]]  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A3 NeuralNetwork Class Using Optimizers.ipynb|A3 NeuralNetwork Class Using Optimizers]] due Thursday, October 6th, at 10:00 PM.    |
-| Week 8:\\  Oct 11, 13  | Pytorch. Convolutional neural nets  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/10 JAX.ipynb|10 JAX]]\\ [[https://nbviewer.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/neuralnetworks_streamlit.tar|neuralnetworks_streamlit.tar]]\\ [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/CNN Backprop.pdf|CNN Backpropagation Notes]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 Introduction to Pytorch.ipynb|12 Introduction to Pytorch]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13 Convolutional Neural Networks in Pytorch.ipynb|13 Convolutional Neural Networks in Pytorch]]  | [[https://moocaholic.medium.com/jax-a13e83f49897|JAX Ecosystem]]\\ [[https://streamlit.io/|Streamlit]]  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A4 Neural Network Classifier.ipynb|A4 Neural Network Classifier]] due Friday, October 14th, at 10:00 PM. <color red>Updated Oct. 11, 2pm, now with A4grader.tar</color> |
+| Week 8:\\  Oct 11, 13  | Pytorch. Convolutional neural nets  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/10 JAX.ipynb|10 JAX]]\\ [[https://nbviewer.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/neuralnetworks_streamlit.tar|neuralnetworks_streamlit.tar]]\\ [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/CNN Backprop.pdf|CNN Backpropagation Notes]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 Introduction to Pytorch.ipynb|12 Introduction to Pytorch]]  | [[https://moocaholic.medium.com/jax-a13e83f49897|JAX Ecosystem]]\\ [[https://streamlit.io/|Streamlit]]\\ [[https://www.deeplearning.ai/blog/acing-data-science-job-interview/?utm_campaign=The%20Batch&utm_medium=email&_hsmi=229461727&_hsenc=p2ANqtz-9bQj7qnAn_EuLfiAfXWztDKramW14RY0e9d9AEJEO_Xb-ABdnYZGPWanYADOLb_2B5GJup_AX4Qr_ge1C-iscdRBPZhAS2ruIHrOjnVo_NesAG0-s&utm_content=229461727&utm_source=hs_email|Breaking Into AI: Sahar Nasiri on Acing the Data Science Job Interview]]  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A4 Neural Network Classifier.ipynb|A4 Neural Network Classifier]] due Friday, October 14th, at 10:00 PM.  |
-| Week 9:\\  Oct 18, 20  | Reinforcement Learning  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/15 Introduction to Reinforcement Learning.ipynb|15 Introduction to Reinforcement Learning]]\\ [[https://nbviewer.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 9:\\  Oct 18, 20  | Convolutional Neural Nets in Pytorch. Reinforcement Learnirng  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13 Convolutional Neural Networks in Pytorch.ipynb|13 Convolutional Neural Networks in Pytorch]] <color red>Updated Oct. 24th</color>\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/15 Introduction to Reinforcement Learning.ipynb|15 Introduction to Reinforcement Learning]]  | [[https://arxiv.org/pdf/2210.08340.pdf|Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution]]      |
-| Week 10:\\  Oct 25, 27  | Reinforcement Learning  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/17 Reinforcement Learning for Two Player Games.ipynb|17 Reinforcement Learning for Two Player Games]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/18 Reinforcement Learning to Control a Marble.ipynb|18 Reinforcement Learning to Control a Marble]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/19 Reinforcement Learning Modular Framework.ipynb|19 Reinforcement Learning Modular Framework]] |  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Project Proposal and Report Example.ipynb|Project Proposal]], due Friday, October 28, 10:00 PM  |
+| Week 10:\\  Oct 25, 27  | Reinforcement Learning  | [[https://nbviewer.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]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/17 Reinforcement Learning for Two Player Games.ipynb|17 Reinforcement Learning for Two Player Games]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/18 Reinforcement Learning to Control a Marble.ipynb|18 Reinforcement Learning to Control a Marble]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/19 Reinforcement Learning Modular Framework.ipynb|19 Reinforcement Learning Modular Framework]] | [[https://lastweekin.ai/p/190?utm_source=substack&utm_medium=email|Last Week in AI]] newsletter, with lots of topics for possible semester projects.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Project Proposal and Report Example.ipynb|Project Proposal]], due Friday, October 28, 10:00 PM  |
 ===== November =====
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 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 11:\\  Nov 1, 3  | 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]]  |  |
+| Week 11:\\  Nov 1, 3  | 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]]  |  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A5 Convolutional Neural Networks.ipynb|A5 Convolutional Neural Networks]] due Friday, November 4th, at 10:00 PM.   |
 | Week 12:\\  Nov 8, 10  | BCI. Recurrent Neural Networks. | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/20 Recurrent Networks in Numpy.ipynb|20 Recurrent Networks in Numpy]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/21 Recurrent Networks in Pytorch.ipynb|21 Recurrent Networks in Pytorch]]\\ [[https://nbviewer.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 15, 17  | K-means clustering. K-nearest-neighbor classification. Support Vector Machines.   | [[https://nbviewer.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]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/24 Support Vector Machines.ipynb|24 Support Vector Machines]]    |