/***

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**.

All students may attend the lecture remotely using [[https://zoom.us/j/92107238733?pwd=Wggv0JQGepdeoezMRrv0gpVImn90yl.1|this zoom link]].

===== August =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| Week 1:\\  Aug 20, 22   | 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 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.  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 =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| 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 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]]   |

===== October =====

|< 100% 18% 20% 22% 20% 20%  >|
^  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. 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 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. 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  | 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 =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| 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]] <color red>Updated Oct 29, 9:30 AM.</color> Due Friday, , November 8th, 10:00 PM.  |
| 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  | 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 Thursday, November 21st, 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. Streamlit.   |   |     | |
| Dec 10-12  |  Final Exam Week  |  No Exams in this course  | |   |