CS545: Machine Learning

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start [2024/10/11 09:32] – [October] andersonstart [2024/12/07 13:34] (current) – external edit 127.0.0.1
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 | Week 3:\\  Sept 3, 5  | 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 3:\\  Sept 3, 5  | 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 10, 12   | Design of NeuralNetwork class. Optimizers. Overview of A2. Memory organization for neural network parameters. Optimizers 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 4:\\  Sept 10, 12   | Design of NeuralNetwork class. Optimizers. Overview of A2. Memory organization for neural network parameters. Optimizers 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 17, 19\\ 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 Thursday, September 19, 10:00 PM. Here is an example solution to A2: [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A2 NeuralNetwork Class Solution.ipynb|A2 NeuralNetwork Class Solution]]  |+| Week 5:\\  Sept 17, 19\\ 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 Thursday, September 19, 10:00 PM. Here is an example solution to A2: [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A2 NeuralNetwork Class Solution.ipynb|A2 NeuralNetwork Class Solution]].\\ Examples of good A2 solutions from your classmates can be [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones/index.html|found here]]  |
 | Week 6:\\  Sept 24, 26  | Classification with 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/11 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|11 Classification with Nonlinear Logistic Regression Using Neural Networks]]   | | Week 6:\\  Sept 24, 26  | Classification with 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/11 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|11 Classification with Nonlinear Logistic Regression Using Neural Networks]]   |
  
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 ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
-| Week 7:\\  Oct 1, 3  | Classification with Nonlinear Logistic Regression, K-Nearest-Neighbors. Clustering with K-Means.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 K-Means Clustering, K-Nearest-Neighbor Classification.ipynb|12 K-Means Clustering, K-Nearest-Neighbor Classification]]  | | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A3 NeuralNetwork Class Using Optimizers.ipynb|A3 NeuralNetwork Class Using Optimizers]] due Friday, October 4th, 10:00 PM. A3grader.zip is now available. |+| Week 7:\\  Oct 1, 3  | Classification with Nonlinear Logistic Regression, K-Nearest-Neighbors. Clustering with K-Means.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 K-Means Clustering, K-Nearest-Neighbor Classification.ipynb|12 K-Means Clustering, K-Nearest-Neighbor Classification]]  | | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A3 NeuralNetwork Class Using Optimizers.ipynb|A3 NeuralNetwork Class Using Optimizers]] due Friday, October 4th, 10:00 PM. A3grader.zip is now available. Examples of good A3 solutions from your classmates can be [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones/index.html|found here]]|
 | Week 8:\\  Oct 8, 10  | A4. Introduction to Reinforcement Learning.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13 Introduction to Reinforcement Learning.ipynb|13 Introduction to Reinforcement Learning]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/14 Reinforcement Learning with Neural Networks as Q Function.ipynb|14 Reinforcement Learning with Neural Networks as Q Function]]  | [[https://www.nytimes.com/2024/10/08/science/nobel-prize-physics.html?campaign_id=9&emc=edit_nn_20241008&instance_id=136314&nl=the-morning&regi_id=78404199&segment_id=179882&te=1&user_id=d288e49a9a2fae84a8aae92c8c269127|John Hopfield and Geoffrey Hinton awarded Nobel Physics Prize]]\\ [[https://www.youtube.com/watch?v=N1TEjTeQeg0|Will digital intelligence replace biological intelligence?]]\\ [[https://www.youtube.com/watch?v=Y6Sgp7y178k|Geoffrey Hinton Warns of the “Existential Threat” of AI]]  | | Week 8:\\  Oct 8, 10  | A4. Introduction to Reinforcement Learning.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13 Introduction to Reinforcement Learning.ipynb|13 Introduction to Reinforcement Learning]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/14 Reinforcement Learning with Neural Networks as Q Function.ipynb|14 Reinforcement Learning with Neural Networks as Q Function]]  | [[https://www.nytimes.com/2024/10/08/science/nobel-prize-physics.html?campaign_id=9&emc=edit_nn_20241008&instance_id=136314&nl=the-morning&regi_id=78404199&segment_id=179882&te=1&user_id=d288e49a9a2fae84a8aae92c8c269127|John Hopfield and Geoffrey Hinton awarded Nobel Physics Prize]]\\ [[https://www.youtube.com/watch?v=N1TEjTeQeg0|Will digital intelligence replace biological intelligence?]]\\ [[https://www.youtube.com/watch?v=Y6Sgp7y178k|Geoffrey Hinton Warns of the “Existential Threat” of AI]]  |
-| Week 9:\\  Oct 15, 17  | Reinforcement learning with Q Function as Neural Network. Learning to play games. |   |  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A4 Neural Network Classifier.ipynb|A4 Neural Network Classifier]] due Wednesday, October 16th, 10:00 PM. <color red>A4grader.zip was updated Oct. 11th, 9:30 am.</color>    | +| Week 9:\\  Oct 15, 17  | Reinforcement learning with Q Function as Neural Network. Learning to play games. | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/14 Reinforcement Learning with Neural Networks as Q Function.ipynb|14 Reinforcement Learning with Neural Networks as Q Function]]\\ [[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://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/16 Targets and Deltas Summary.ipynb|16 Targets and Deltas Summary]]  |  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A4 Neural Network Classifier.ipynb|A4 Neural Network Classifier]] due Friday, October 18th, 10:00 PM.  Examples of good A4 solutions from your classmates can be [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones/index.html|found here]]    | 
-| Week 10:\\  Oct 22, 24  | Modular framework for reinforcement learning. Convolutional Neural Networks.   |   | |   | +| Week 10:\\  Oct 22, 24  | Modular framework for reinforcement learning. Parallel processing with ray. Introductions to Pytorch and Jax.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/17 Modular Framework for Reinforcement Learning.ipynb|17 Modular Framework for Reinforcement Learning]]\\ [[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 More Tic-Tac-Toe and a Simple Robot Arm.ipynb|19 More Tic-Tac-Toe and a Simple Robot Arm]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/20 Introduction to Jax.ipynb|19 Introduction to Jax]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/21 Introduction to Pytorch.ipynb|21 Introduction to 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/Project Proposal and Report Example.ipynb|Project proposal]] due at 10 pm Friday evening, October 25th.   | 
-| Week 11:\\  Oct 29, 31  | 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]]  |   |+| Week 11:\\  Oct 29, 31  | A5.\\ Pytorch.\\ Convolutions.    | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/22 Introduction to Convolutional Neural Networks.ipynb|22 Introduction to Convolutional Neural Networks]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/23 Convolutional Neural Network in Numpy.ipynb|23 Convolutional Neural Network in Numpy]]  | [[https://medium.com/@mayank.utexas/backpropagation-for-convolution-with-strides-8137e4fc2710|Backpropagation for Convolution]] by Mayank Kaushik\\ [[https://pavisj.medium.com/convolutions-and-backpropagations-46026a8f5d2c|Convolutions and Backpropagations]] by Pavithra Solai  |   |
  
 ===== November ===== ===== November =====
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 ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
-| Week 12:\\  Nov 5, 7  | Convolutional Neural Networks.  |  | | +| Week 12:\\  Nov 5, 7  | Convolutional Neural Networks in Pytorch  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/24 Convolutional Neural Network Class in Pytorch.ipynb|24 Convolutional Neural Network Class in Pytorch]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/25 CNN on One-Dimensional Data.ipynb|25 CNN on One-Dimensional Data]]   | | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A5 Pole Balancing with Reinforcement Learning.ipynb|A5 Pole Balancing with Reinforcement Learning]]  Due Friday, November 8th, 10:00 PM. Examples of good A5 solutions  can be [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones/index.html|found here]] | 
-| Week 13:\\  Nov 12, 14  | Ensembles. Mixture of Experts.   |   |   | +| Week 13:\\  Nov 12, 14  | Ensembles. Autoencoders. Recurrent Neural Networks.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/26 Ensembles.ipynb|26 Ensembles]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/27 Autoencoders.ipynb|27 Autoencoders]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/28 Recurrent Networks in Numpy.ipynb|28 Recurrent Networks in Numpy]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/29 Recurrent Networks in Pytorch.ipynb|29 Recurrent Networks in Pytorch]]   |   | 
-| Week 14:\\  Nov 19, 21  | Clustering. K-Nearest Neighbors. Web Apps with Streamlit.  |   | [[https://www.nature.com/articles/d41586-023-03635-w|ChatGPT generates fake data set to support scientific hypothesis]]  |   |+| Week 14:\\  Nov 19, 21  | Word Embeddings. Transformers. Support Vector Machines.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/30 Word Embeddings.ipynb|30 Word Embeddings]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/31 Introduction to Transformers.ipynb|31 Introdcution to Transformers]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/32 Support Vector Machines.ipynb|32 Support Vector Machines]]  | [[https://www.nature.com/articles/d41586-023-03635-w|ChatGPT generates fake data set to support scientific hypothesis]]\\ [[https://arxiv.org/pdf/2411.06469|ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?]]\\ [[https://www.statology.org/f1-score-vs-accuracy/|F1 Score vs. Accuracy: Which Should You Use?]]  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A6 Convolutional Neural Networks.ipynb|A6 Convolutional Neural Networks]] Due Saturday, November 23rd, 10:00 PM.      |
 | Fall Break:\\ Nov 25-29 | No classes.  | | Fall Break:\\ Nov 25-29 | No classes.  |
  
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 ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
-| Week 15:\\  Dec 3, 5  | Word embeddings. Transformers.  |   |     | | +| Week 15:\\  Dec 3, 5  | Mixture of Experts.  Streamlit.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/33 Mixture of Experts.ipynb|33 Mixture of Experts]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/34 Drawing Digits.ipynb|34 Drawing Digits]]\\ \\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/35 Web Apps with Streamlit.ipynb|35 Web Apps With Streamlit]]   |     | | 
-| Dec 10-12  |  Final Exam Week  |  No Exams in this course  | |   |+| Dec 10-12  |  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 Wednesday evening, December 11th.   |
  
  
  
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