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--- schedule [2022/11/30 12:45] – external edit 127.0.0.1
+++ schedule [2023/12/07 09:17] (current) – external edit 127.0.0.1
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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.
+***/
+ \\
+ \\
+ \\
 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      ^  Topic      ^  Lecture Notes  ^  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 1:\\  Aug 22, 24   | Course overview. 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/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!  \\  [[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 NS| Not 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.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  |
+| Week 2:\\  Aug 29, 31  | Jupyter notebook animations. Optimization algorithms. Simple linear and nonlinear models.   | [[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://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/02a Generative AI--Friend or Foe.ipynb|02a 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 Searching for Good Weights in a Linear Model]]  |   |  |
 ===== September =====
 |< 100% 18% 20% 22% 20% 20%  >|
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
+^  Week      ^  Topic      ^  Lecture Notes  ^  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 3:\\  Sept 5, 7\\ Chuck's office hours Thursday will be from 2 to 3:30.  | Confidence intervals. Introduction to neural networks.  | [[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]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/05 Introduction to Neural Networks.ipynb|05 Introduction to Neural Networks]] | | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A1.ipynb|A1]] due Friday, September 8th, 10:00 PM  |
-| 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 4:\\  Sept 12, 14   | Design of NeuralNetwork class. Optimizers.  | [[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.ipynb|07 Optimizers]]   | [[https://machinelearningmastery.com/weight-initialization-for-deep-learning-neural-networks/|Weight Initialization for Deep Learning Neural Networks]], by Jason Brownlee  |
-| 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 5:\\  Sept 19, 21  | Using optimizers.  | [[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/A2 NeuralNetwork Class.ipynb|A2 NeuralNetwork Class]] due Thursday, September 21st, 10:00 PM.  Examples of good A2 solutions can be [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|found here]]  |
-| 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]]  |  |  |
+| Week 6:\\  Sept 26, 28  | Early stopping (new version of optimizers). A3. Introduction to classification.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/07a Optimizers2.ipynb|07a Optimizers2]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/09 Introduction to Classification.ipynb|09 Introduction to Classification]]\\ Tuesday lecture pre-recorded and available now on Echo360.  |
 ===== October =====
 |< 100% 18% 20% 22% 20% 20%  >|
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
+^  Week      ^  Topic      ^  Lecture Notes  ^  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 7:\\  Oct 3, 5  | Classification with QDA, LDA, and linear logistic regression.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/10 Classification with Linear Logistic Regression.ipynb|10 Classification with Linear Logistic Regression]]  | | [[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 5th, 10:00 PM\\ Examples of good A3 solutions can be [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|found here]] |
-| 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 8:\\  Oct 10, 12  | Classification with Nonlinear Logistic Regression. Introduction to Reinforcement Learning.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/11 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|11 Classification with Nonlinear Logistic Regression Using Neural Networks]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 Introduction to Reinforcement Learning.ipynb|12 Introduction to Reinforcement Learning]]  | |
-| 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 9:\\  Oct 17, 19  | Reinforcement learning with Q Function as Neural Network. Learning to play games. | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13 Reinforcement Learning with Neural Networks as Q Function.ipynb|13 Reinforcement Learning with Neural Networks as Q Function]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/14 Targets and Deltas Summary.ipynb|14 Targets and Deltas Summary]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/15 Reinforcement Learning for Two Player Games.ipynb|15 Reinforcement Learning for Two Player 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]]  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A4 Neural Network Classifier.ipynb|A4 Neural Network Classifier]] due Wednesday, October 18th, 10:00 PM\\ Examples of good A4 solutions can be [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|found here]]\\ And here are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/neuralnetworksA4.zip|python script files]] as A4 answer.   |
-| 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  |
+| Week 10:\\  Oct 24, 26  | Modular framework for reinforcement learning. Convolutional Neural Networks.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/16 Modular Framework for Reinforcement Learning.ipynb|16 Modular Framework for Reinforcement Learning]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/17 Convolutional Neural Networks.ipynb|17 Convolutional Neural Networks]]  | | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Project Proposal and Report Example.ipynb|Project proposal]] due at 10 pm Friday evening, October 27th.  |
 ===== November =====
 |< 100% 18% 20% 22% 20% 20%  >|
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
+^  Week      ^  Topic      ^  Lecture Notes  ^  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 11:\\  Oct 31 Nov 2  | Ray. Pytorch.  Convolutional Neural Networks.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/18 Ray for Parallel Processing.ipynb|18 Ray for Parallel Processing]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/19 Introduction to Pytorch.ipynb|19 Introduction to Pytorch]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/20 Steps Towards Convolutional Nets in Pytorch.ipynb|20 Steps Towards Convolutional Nets in Pytorch]]  | [[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]]  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A5 Reinforcement Learning.ipynb|A5 Reinforcement Learning]] due Friday, Nov 3rd, 10:00 PM. <color red>This notebook and a5.zip were updated slightly Oct. 26th, 11:00 AM. </color>  |
-| 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 12:\\  Nov 7, 9  | Convolutional Neural Networks. Ensembles.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/21 Convolutional Neural Network Class in Pytorch.ipynb|21 Convolutional Neural Network Class in Pytorch]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/22 Ensembles.ipynb|22 Ensembles]]  | |
-| 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.  |
+| Week 13:\\  Nov 14, 16  | Clustering. K-Nearest Neighbors. Jax.  | [[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 Introduction to Jax.ipynb|24 Introduction to Jax]]   | [[https://www.science.org/doi/10.1126/science.adi2336|Learning skillful medium-range global weather forecasting]] by DeepMind, using graph-convolutional_networks (GNNs) implemented in with jax.  |
-| Fall Break:\\ Nov 21-25 |
+| Fall Break:\\ Nov 20-24 | No classes.  |
-| 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]]   |
+| Week 14:\\  Nov 28, 30  | Support Vector Machines. Web Apps with Streamlit. Word Embeddings.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/25 Support Vector Machines.ipynb|25 Support Vector Machines]]\\  [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/26 Web Apps with Streamlit.ipynb|26 Web Apps with Streamlit]]\\  [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/27 Word Embeddings.ipynb|27 Word Embeddings]]  | [[https://www.nature.com/articles/d41586-023-03635-w|ChatGPT generates fake data set to support scientific hypothesis]]  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A6 Convolutional Neural Networks.ipynb|A6 Convolutional Neural Networks]] due Friday, Dec. 1, 10:00 PM.  |
 ===== December =====
 |< 100% 18% 20% 22% 20% 20%  >|
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
+^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
-| Week 15:\\  Dec 6, 8  | GTA Saira Jabeen summarizes her research.  Support Vector Machines. Introduction to Transformers  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/26 Support Vector Machines.ipynb|26 Support Vector Machines]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/27 Introduction to Transformers.ipynb|27 Introduction to Transformers]] |   |  |
+| Week 15:\\  Dec 5, 7  | Transformers.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/28 Introduction to Transformers.ipynb|28 Introduction to Transformers]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/29 Transformers Predicting Text.ipynb|29 Transformers Predicting Text]]\\ Chuck's Recent Projects:\\ 1. Pretraining Speeds Up RL\\  2. Brain-Computer Interfaces\\  3. Explaining a Neural Network's Decisions   | [[https://colab.research.google.com/drive/1Qhq2FO4FFX_MKN5IEgia_PrBEttxCQG4?usp=sharing&utm_source=substack&utm_medium=email#scrollTo=r_20uEgdp4W_|Automatic detection of hallucination with SelfCheckGPT]]\\ [[https://arxiv.org/pdf/2309.13638.pdf|Embers of Autoregression: Understanding Large Language
-| 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.]]   |
+Models Through the Problem They are Trained to Solve]]    | |
+| Dec 11-15  |  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 at 10 pm Tuesday evening, December 12th.  |