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--- schedule [2024/08/19 10:36] – anderson
+++ schedule [2024/09/24 09:56] (current) – external edit 127.0.0.1
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 ^  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  | Jupyter notebook animations. Optimization algorithms. Simple linear and nonlinear models.   |   |   |  |
+| 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 =====
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 |< 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.  |  | |  |
+| 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.  |  | [[https://machinelearningmastery.com/weight-initialization-for-deep-learning-neural-networks/|Weight Initialization for Deep Learning Neural Networks]], by Jason Brownlee  |
+| 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  | Using optimizers.  |   | |   |
+| 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 <color red>Thursday, September 19, 10:00 PM.</color> Notebook and A2grader updated Sept. 12, 5:30 pm.   |
-| Week 6:\\  Sept 24, 26  | Early stopping (new version of optimizers). A3. Introduction to classification.   |   |
+| 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 =====
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 |< 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.  |  | |  |
+| Week 7:\\  Oct 1, 3  | Classification with QDA, LDA, and 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 Tuesday, October 1st, 10:00 PM. |
 | Week 8:\\  Oct 8, 10  | Classification with Nonlinear Logistic Regression. Introduction to Reinforcement Learning.  |  | |
 | Week 9:\\  Oct 15, 17  | Reinforcement learning with Q Function as Neural Network. Learning 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]]  |    |