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start [2022/10/01 17:21] andersonstart [2024/05/20 17:22] (current) – external edit 127.0.0.1
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 ***/ ***/
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 +/***
 +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.
 +***/
 + \\ 
 + \\ 
 + \\ 
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 The following schedule is **tentative and is being updated**. 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 ===== ===== August =====
  
 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
-^  Week      ^  Topic      ^  Material   Reading          ^  Assignments +^  Week      ^  Topic      ^  Lecture Notes   Reading          ^  Assignments 
-| Week 1:\\  Aug 23256   | Overview of courseReview of neural networks training and use.  | [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/01 Introduction to CS545.ipynb|01 Introduction to CS545]]\\ [[https://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]]   | [[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 1:\\  Aug 2022   | Course overviewJupyter notebooks.     | [[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!  \\  [[https://www.freecodecamp.org/news/exploratory-data-analysis-with-numpy-pandas-matplotlib-seaborn/|What is Data Analysis? How to Visualize Data with Python, Numpy, Pandas, Matplotlib & Seaborn Tutorial]], by Aakash NSNot graded: Please fill out [[https://forms.gle/hppJ5QuRFuRn1L2h7|this anonymous survey]] before Thursday class. 
-| 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.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]]  |  |[[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Quiz1.ipynb|Quiz 1]] due Wednesday, August 31, 10:00 PMin Canvas  | +| Week 2:\\  Aug 2729  | Jupyter notebook animations. Optimization algorithms. Simple linear and nonlinear models.        |
  
 ===== September ===== ===== September =====
  
 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
-^  Week      ^  Topic      ^  Material   Reading          ^  Assignments +^  Week      ^  Topic      ^  Lecture Notes   Reading          ^  Assignments 
-| Week 3:\\  Sept 68  | Introduction to Neural Networks  [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/04 Introduction to Neural Networks.ipynb|04 Introduction to Neural Networks]]\\ [[https://nbviewer.ipython.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 3:\\  Sept 35\\ Chuck's office hours Thursday will be from 2 to 3:30.  Confidence intervals. Introduction to neural networks  | |  | 
-| Week 4:\\  Sept 1315\\   |  Python classes A2   | [[https://nbviewer.ipython.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.ipython.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.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/solutions/Anderson-Solution-A1.ipynb|Anderson-Solution-A1]]  | +| Week 4:\\  Sept 1012   | Design of NeuralNetwork classOptimizers.  |  | [[https://machinelearningmastery.com/weight-initialization-for-deep-learning-neural-networks/|Weight Initialization for Deep Learning Neural Networks]], by Jason Brownlee  | 
-| Week 5:\\  Sept 2022  Optimizers. Autoencoders.  | [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/05 Optimizers.ipynb|05 Optimizers]] <color red>Updated Sept. 27th</color> \\ [[https://nbviewer.ipython.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.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/06 Autoencoders.ipynb|06 Autoencoders]]\\ [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/06a Visualizing Weights.ipynb|06a Visualizing Weights]] <color red>New</color>  | [[https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf|Pandas Cheat Sheet]]  | [[https://nbviewer.ipython.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.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/solutions/Anderson-A2-Solution.ipynb|Anderson-A2-Solution]]  +| Week 5:\\  Sept 1719  Using optimizers.  |   | |   
-| Week 6:\\  Sept 2729   A3. Classification  | [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/07 Introduction to Classification.ipynb|07 Introduction to Classification]]  | [[https://moocaholic.medium.com/jax-a13e83f49897|JAX Ecosystem]]\\ [[https://streamlit.io/|Streamlit]]  |  |+| Week 6:\\  Sept 2426  Early stopping (new version of optimizers). A3. Introduction to classification    |
  
 ===== October ===== ===== October =====
  
 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
-^  Week      ^  Topic      ^  Material   Reading          ^  Assignments +^  Week      ^  Topic      ^  Lecture Notes   Reading          ^  Assignments 
-| Week 7:\\  Oct 4 | Classification. Convolutional neural networks.  | [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/08 Classification with Linear Logistic Regression.ipynb|08 Classification with Linear Logistic Regression]]\\ [[https://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]]\\ [[https://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://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]]  [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A3 NeuralNetwork Class Using Optimizers.ipynb|A3 NeuralNetwork Class Using Optimizers]] due Tuesday, October 4th, at 10:00 PM. <color red>Updated Sept. 27th.</color>   +| Week 7:\\  Oct 1 | Classification with QDA, LDA, and linear logistic regression.  |  | |  | 
-| Week 8:\\  Oct 1113  PytorchConvolutional neural nets  | [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 Introduction to Pytorch.ipynb|12 Introduction to Pytorch]]\\ [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13 Convolutional Neural Networks in Pytorch.ipynb|13 Convolutional Neural Networks in Pytorch]]\\ [[https://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 8:\\  Oct 8, 10  | Classification with Nonlinear Logistic Regression. Introduction to Reinforcement Learning.  |  | | 
-| Week 9:\\  Oct 1820  Reinforcement Learning  | [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/15 Introduction to Reinforcement Learning.ipynb|15 Introduction to Reinforcement Learning]]\\ [[https://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 9:\\  Oct 1517  Reinforcement learning with Q Function as Neural NetworkLearning to play games. |   | [[https://lastweekin.ai/p/241|Last Week in AI]]\\ [[https://www.cbsnews.com/news/geoffrey-hinton-ai-dangers-60-minutes-transcript/?utm_source=substack&utm_medium=email|Geoffrey Hinton: AI Dangers, on 60 Minutes]]  |    
-| Week 10:\\  Oct 2527  Reinforcement Learning  | [[https://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]]\\ [[https://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]]\\ [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/19 Reinforcement Learning Modular Framework.ipynb|19 Reinforcement Learning Modular Framework]] |  +| Week 10:\\  Oct 2224  Modular framework for reinforcement learningConvolutional Neural Networks    | |   | 
 +| Week 11:\\  Oct 2931  RayPytorch Convolutional Neural Networks   | [[https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/|President Biden's Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence]]    |
  
 ===== November ===== ===== November =====
  
 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
-^  Week      ^  Topic      ^  Material   Reading          ^  Assignments +^  Week      ^  Topic      ^  Lecture Notes   Reading          ^  Assignments 
-| Week 11:\\  Nov 1 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 12:\\  Nov 5 Convolutional Neural Networks. Ensembles.  |  | | 
-| Week 12:\\  Nov 810  BCIRecurrent Neural Networks| [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/20 Recurrent Networks in Numpy.ipynb|20 Recurrent Networks in Numpy]]\\ [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/21 Recurrent Networks in Pytorch.ipynb|21 Recurrent Networks in Pytorch]]\\ [[https://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 1214  ClusteringK-Nearest NeighborsJax.  |     
-| Week 13:\\  Nov 1517  K-means clustering. K-nearest-neighbor classification. Support Vector Machines.   | [[https://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]]\\ [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/24 Support Vector Machines.ipynb|24 Support Vector Machines]]      +| Week 14:\\  Nov 1921  | Support Vector Machines. Web Apps with Streamlit. Word Embeddings.   | [[https://www.nature.com/articles/d41586-023-03635-w|ChatGPT generates fake data set to support scientific hypothesis]]    
-Week 14:\\  Nov 29, Dec 1  Introduction to Transformers  | [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/25 Introduction to Transformers.ipynb|25 Introduction to Transformers]] +Fall Break:\\ Nov 25-29 | No classes.  |
  
 ===== December ===== ===== December =====
  
 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
-^  Week      ^  Topic      ^  Material   Reading          ^  Assignments +^  Week      ^  Topic      ^  Lecture Notes   Reading          ^  Assignments 
-| Week 15:\\  Dec 6 | Transformers: Self-Attention Replaced by Fourier Transform.\\ Cascade Ensemble Network   | [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/26 FNet--Replace Self-Attention with Fourier Transform.ipynb|26 FNet--Replace Self-Attention with Fourier Transform]]\\ [[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/27 Cascade Ensemble Network.ipynb|27 Cascade Ensemble Network]] |  +| Week 15:\\  Dec 3 | Transformers.    |     | | 
-| Dec 12-16   Final Exam Week  |  No Exams in this course +| Dec 10-12  |  Final Exam Week  |  No Exams in this course  | |   |
  
  
  
start.txt · Last modified: 2024/05/20 17:22 by 127.0.0.1