CS545: Machine Learning

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/***

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

***/

/***
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**.


===== August =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| Week 1:\\  Aug 20, 22   | Course overview. Jupyter notebooks. Animations.    | [[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 27, 29  | Optimization algorithms. Simple linear and nonlinear models.   | [[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]]  |   |  |

===== September =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| Week 3:\\  Sept 3, 5\\ 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 10, 12   | 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 17, 19  | 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 24, 26  | 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      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| Week 7:\\  Oct 1, 3  | 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 8, 10  | 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 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/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 22, 24  | 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.  |
| Week 11:\\  Oct 29, 31  | 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.  |

===== November =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| Week 12:\\  Nov 5, 7  | 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 12, 14  | 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.  |
| Week 14:\\  Nov 19, 21  | 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.  |
| Fall Break:\\ Nov 25-29 | No classes.  |

===== December =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| Week 15:\\  Dec 3, 5  | 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
Models Through the Problem They are Trained to Solve]]    | |
| 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 Tuesday evening, December 12th.  |