CS545: Machine Learning

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start [2023/07/03 18:09] – andersonstart [2023/10/26 15:58] – [October] anderson
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 ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
-| Week 1:\\  Aug 22, 24   |  | | | +| 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 29, 30  |  | | |+| 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 ===== ===== September =====
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 ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
-| Week 3:\\  Sept 5, 7  |  | | | | +| 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 12, 14   |  | | | +| 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 19, 21  |  | | | +| 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 26, 28  |  | | |+| 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 ===== ===== October =====
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 ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
-| Week 7:\\  Oct 3, 5  |  | | | +| 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 10, 12  |  | | | +| 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 17, 19  | | | | +| 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  | 
-| Week 10:\\  Oct 24, 26  |  | | |+| Week 10:\\  Oct 24, 26  | Modular framework for reinforcement learning. Convolutional Neural Networks. Pytorch.  | [[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/18 Introduction to Pytorch.ipynb|18 Introduction to Pytorch]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/19 Convolutional Neural Networks in Pytorch.ipynb|19 Convolutional Neural Networks in Pytorch]]  | | [[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 ===== ===== November =====
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 ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
-| Week 11:\\  Oct 31 Nov 2  |  | | | +| Week 11:\\  Oct 31 Nov 2  | Recurrent neural networks.  | | | [[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 7, 9  | | | | +| Week 12:\\  Nov 7, 9  | Unsupervised learning. Dimensionality reduction. Autoencorders. | | | 
-| Week 13:\\  Nov 14, 16  |  | | |+| Week 13:\\  Nov 14, 16  | Clustering.  | | |
 | Fall Break:\\ Nov 20-24 | No classes  | | Fall Break:\\ Nov 20-24 | No classes  |
-| Week 14:\\  Nov 28, 30  | | | |+| Week 14:\\  Nov 28, 30  | Ensemble methods. Mixture-of-experts. Transformers.  | | |
  
 ===== December ===== ===== December =====
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 ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
-| Week 15:\\  Dec 5, 7  | | | |+| Week 15:\\  Dec 5, 7  | Other topics in current research.  | | [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/2023_AI-scientists-topline-report81.pdf|AI Scientists’ Perspectives on AI]]  |
 | Dec 11-15  |  Final Exam Week  |  No Exams in this course  |  | Dec 11-15  |  Final Exam Week  |  No Exams in this course  | 
  
  
  
start.txt · Last modified: 2024/05/20 17:22 by 127.0.0.1