/***

To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:

  export PATH=/usr/local/anaconda3/latest/bin:$PATH

***/

/*** The following schedule is **tentative and is being updated**.  **/

Please send your suggestions regarding lecture topics to Chuck using [[https://tinyurl.com/2nyfzc36|this Google Docs form]].  Questions regarding assignments should be entered in Canvas discussions.

===== August =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
| Week 1:\\  Aug 23, 256   | Overview of course. Review of neural networks training and use.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/01 Introduction to CS545.ipynb|01 Introduction to CS545]]\\ [[https://nbviewer.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]]   | [[https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html|JupyterLab Introduction]], watch the video then play with jupyter lab.  \\ [[https://tinyurl.com/2qw45tlp|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.  | [[https://nbviewer.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]]  |  |[[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Quiz1.ipynb|Quiz 1]] due Wednesday, August 31, 10:00 PM, in Canvas  |


===== September =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
| Week 3:\\  Sept 6, 8  | Introduction to Neural Networks  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/04 Introduction to Neural Networks.ipynb|04 Introduction to Neural Networks]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/04a Simple Animations.ipynb|04a Simple Animations]]\\   | [[https://doi.org/10.1016/j.neucom.2022.06.111|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.    | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/04b Introduction to Python Classes.ipynb|04b Introduction to Python Classes]]  | [[https://docs.python.org/3/tutorial/classes.html|Classes Tutorial]]  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A1 Three-Layer Neural Network.ipynb|A1 Three-Layer Neural Network]] due Monday, Sept 12th, at 10:00 PM\\  [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/solutions/Anderson-Solution-A1.ipynb|Anderson-Solution-A1]]  |
| Week 5:\\  Sept 20, 22  | Optimizers. Autoencoders.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/05 Optimizers.ipynb|05 Optimizers]] \\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/05a Collecting All Weights into One-Dimensional Vector for Use in Optimizers.ipynb|05a Collecting All Weights into One-Dimensional Vector for Use in Optimizers]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/06 Autoencoders.ipynb|06 Autoencoders]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/06a Visualizing Weights.ipynb|06a Visualizing Weights]]   | [[https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf|Pandas Cheat Sheet]]  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A2 NeuralNetwork Class.ipynb|A2 NeuralNetwork Class]] due Thursday, Sept 22nd, at 10:00 PM\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/solutions/Anderson-A2-Solution.ipynb|Anderson-A2-Solution]]  |
| Week 6:\\  Sept 27, 29  |  A3. Classification  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/07 Introduction to Classification.ipynb|07 Introduction to Classification]]  |  |  |

===== October =====

|< 100% 18% 20% 22% 20% 20%  >|
^  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://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. A4 solution available [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/A4solution.tar|here as A4solution.tar]], and here are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|examples of good solutions.]]  |
| 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]] \\ [[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/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://lastweekin.ai/p/190?utm_source=substack&utm_medium=email|Last Week in AI]] newsletter, with lots of topics for possible semester projects.\\ [[https://www.cell.com/neuron/fulltext/S0896-6273(22)00806-6#%20|Pong in a dish]]  | [[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 =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
| Week 11:\\  Nov 1, 3  | Reinforcement Learning for control dynamical systems.  Transfer learning in Reinforcement Learning.    | [[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/20 Reinforcement Learning to Control a Marble Variable Goal.ipynb|20 Reinforcement Learning to Control a Marble Variable Goal]]  | [[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.\\ Here are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|examples of good solutions.]]   |
| Week 12:\\  Nov 8, 10  | Brain-Computer Interfaces. Linear dimensionality reduction. | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/21 Linear Dimensionality Reduction with PCA.ipynb|21 Linear Dimensionality Reduction with PCA]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/22 Linear Dimensionality Reduction with Sammon Mapping.ipynb|22 Linear Dimensionality Reduction with Sammon Mapping]]  | |  |
| Week 13:\\  Nov 15, 17  | Recurrent neural networks.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/23 Recurrent Neural Networks.ipynb|23 Recurrent Neural Networks]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/24 Recurrent Network Applications.ipynb|24 Recurrent Network Applications]]  |  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A6 Reinforcement Learning to Control a Robot.ipynb|A6 Reinforcement Learning to Control a Robot]] due Friday, November 18th, at 10:00 PM. [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|Examples of good solutions]]  |
| Fall Break:\\ Nov 21-25 |
| Week 14:\\  Dec 1  | K-means clustering. K-nearest-neighbor classification.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/25 K-Means Clustering, K-Nearest-Neighbor Classification.ipynb|25 K-Means Clustering, K-Nearest-Neighbor Classification]]   |

===== December =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
| Week 15:\\  Dec 6, 8  | GTA Saira Jabeen summarizes her research.  Support Vector Machines.  Understanding what a neural net has learned using optimal inputs.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/26 Support Vector Machines.ipynb|26 Support Vector Machines]]\\ [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/talk_NeurIPS 2022.pdf|An Interpretable Model of Climate Change Using Correlative Learning: Slides]]  | [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/CMIP6_with_Alopex.pdf|An Interpretable Model of Climate Change Using Correlative Learning: Paper]]\\ [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/Alopex.pdf|Alopex: A Correlation-Based Learning Algorithm for Feedforward and Recurrent Neural Networks: Paper]]   |  | 
| Dec 12-16  |  Final Exam Week  |  No Exams in this course  | |[[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Project Proposal and Report Example.ipynb|Project Report]], due Monday, December 12th, 10:00 PM.  [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/titles.html|Here is a list of project titles and authors.]]   |