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start [2022/09/07 10:56] – [September] andersonstart [2024/09/19 10:43] (current) – [September] anderson
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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.+All students may attend the lecture remotely using [[https://zoom.us/j/92107238733?pwd=Wggv0JQGepdeoezMRrv0gpVImn90yl.1|this zoom link]].
  
 ===== 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 | [[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]]   | [[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. YayColorado!     +| Week 1:\\  Aug 2022   | Course overview \\ Machine Learning and AI: History and Present Boom\\ Jupyter notebooks   | [[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/01a Simple Animations.ipynb|01a Simple Animations]]\\  [[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://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 30Sept 1  Thursday lecture cancelled. Please watch pre-recorded lecture in Echo360Quiz1 and A1 questions. 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]]   |[[https://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Quiz1.ipynb|Quiz 1]] due Wednesday, August 31, 10:00 PM, in Canvas  | +| Week 2:\\  Aug 2729  Optimization algorithmsSimple linear and nonlinear models.  Confidence intervals.   | [[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://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/02a Input Importance and Generative AI---Friend or Foe.ipynb|02a Input Importance and Generative AI---Friend or Foe]] \\ [[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/04 Training Multiple Models to Obtain Confidence Intervals.ipynb|04 Training Multiple Models to Obtain Confidence Intervals]]   |    |
  
 ===== 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 6 OptimizersNeural Network class.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/04 Introduction to Neural Networks.ipynb|04 Introduction to Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/05 Optimizers.ipynb|05 Optimizers]]  | [[https://doi.org/10.1016/j.neucom.2022.06.111|Activation functions in deep learning: A comprehensive survey and benchmark]], Neurocomputing, volume 503, 2022, pp92-108    | +| Week 3:\\  Sept 3 Introduction to neural networks  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/05 Introduction to Neural Networks.ipynb|05 Introduction to Neural Networks]]  [[https://www.3blue1brown.com/topics/neural-networks|3Blue1Brown Introduction to Neural Networks]] in the first five chapters provides a fun video tutorial including error backpropagation.  |   | 
-| Week 4:\\  Sept 1315  | A2. AutoencodersClassification  | [[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]]  | | [[http://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   | +| Week 4:\\  Sept 1012   Design of NeuralNetwork class. Optimizers. Overview of A2. Memory organization for neural network parametersOptimizers tailored for neural networks | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/06 Python Classes.ipynb|06 Python Classes]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/07 Optimizers Simple.ipynb|07 Optimizers Simple]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/08 Collecting All Weights into One-Dimensional Vector for Use in Optimizers.ipynb|08 Collecting All Weights into One-Dimensional Vector for Use in Optimizers]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/08a Optimizers.ipynb|08a Optimizers]]   | [[https://machinelearningmastery.com/weight-initialization-for-deep-learning-neural-networks/|Weight Initialization for Deep Learning Neural Networks]], by Jason Brownlee  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A1.ipynb|A1]] due Monday, September 9th, 10:00 PM.  | 
-| Week 5:\\  Sept 20, 22  | 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]]  |   +| Week 5:\\  Sept 1719\\ Chuck's office hours cancelled today.  Introduction to Classification.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/09 Introduction to Classification.ipynb|09 Introduction to Classification]]    | | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A2 NeuralNetwork Class.ipynb|A2 NeuralNetwork Class]] due <color red>Thursday, September 19, 10:00 PM.</color> Notebook and A2grader updated Sept12, 5:30 pm  | 
-| Week 6:\\  Sept 2729    | [[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]]  | |  +| Week 6:\\  Sept 24, 26  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 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]]  +| Week 7:\\  Oct 1 Classification with QDA, LDA, and linear logistic regression.  |  | |  | 
-| Week 8:\\  Oct 1113  PytorchConvolutional 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 8:\\  Oct 8, 10  | Classification with Nonlinear Logistic RegressionIntroduction to Reinforcement Learning |  | | 
-| Week 9:\\  Oct 1820  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 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  | [[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]] |  +| Week 10:\\  Oct 2224  Modular framework for reinforcement learningConvolutional Neural Networks    | |   | 
 +| Week 11:\\  Oct 2931  Pytorch.\\ Jax.\\ Ray     | [[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.  |  | | 
-| Week 12:\\  Nov 810  BCIRecurrent 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 1214  EnsemblesMixture of Experts      
-| Week 13:\\  Nov 1517  K-means clustering. K-nearest-neighbor classificationSupport 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]]      +| Week 14:\\  Nov 1921  Clustering. K-Nearest NeighborsWeb Apps with Streamlit   | [[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  | [[http://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   | [[http://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]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/27 Cascade Ensemble Network.ipynb|27 Cascade Ensemble Network]] |  +| Week 15:\\  Dec 3 Word embeddings. Transformers.    |     | | 
-| Dec 12-16   Final Exam Week  |  No Exams in this course +| Dec 10-12   Final Exam Week  |  No Exams in this course  | |   |
  
  
  
start.1662569788.txt.gz · Last modified: 2022/09/07 10:56 by anderson