User Tools

Site Tools


schedule

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
schedule [2021/05/17 14:54]
anderson [Schedule]
schedule [2021/09/15 15:38] (current)
Line 11: Line 11:
  
 ***/ ***/
 +
 +/***
  
 To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file: To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:
Line 16: Line 18:
   export PATH=/usr/local/anaconda/bin:$PATH   export PATH=/usr/local/anaconda/bin:$PATH
  
-This is a tentative schedule of CS545 topics for Fall, 2020.  This will be updated during the summer and as the fall semester continues.+***/ 
 + 
 +This tentative schedule will be updated during the semester. 
  
 ===== August ===== ===== August =====
Line 22: Line 27:
 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 1:\\  Aug 24 - Aug 28    | Overview of course and the machine learning fieldReminder of how python is used in machine learning.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/01 Introduction to CS545.ipynb|01 Introduction to CS545]]\\ [[http://nbviewer.ipython.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]]   | [[http://www.labri.fr/perso/nrougier/from-python-to-numpy/|From Python to Numpy]], Chapters 1 - 2\\ [[http://www.scipy-lectures.org/|Scipy Lectures]], Section 1\\ [[https://jakevdp.github.io/PythonDataScienceHandbook/04.00-introduction-to-matplotlib.html|Visualization with Matplotlib]]\\ [[http://www.deeplearningbook.org/|Deep Learning]], Chapters 1 - 5.1.4  |+| Week 1:\\  Aug 24, 26   | Overview of course. Review of neural networks training and use.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/01 Introduction to CS545.ipynb|01 Introduction to CS545]]\\ [[http://nbviewer.ipython.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]]    | <color red>Ungraded</color> [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Quiz1.ipynb|Quiz 1]] due FridayAugust 27, 10:00 PM  | 
 +| Week 2:\\  Aug 31, Sept 2  | Regression with neural networks. [[http://nbviewer.ipython.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]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/04 Introduction to Neural Networks.ipynb|04 Introduction to Neural Networks]]  |
  
  
Line 29: Line 35:
 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 2:\\ Aug 31 - Sept 4    Help with A1. Review of gradientsGradient descent with SGD, Adam and SCG,  | [[http://nbviewer.ipython.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]]  |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A1.4 Polynomial Model.ipynb|A1.4 Polynomial Model]] due Friday, Sept 4th, at 10:00 PM  | +| Week 3:\\  Sept 7, 9  | A1 questionsOptimizers. Neural Network class.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/05 Optimizers.ipynb|05 Optimizers]]  | | <color red>Updated Sept. 6, 5:00 PM:</color> [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A1 Three-Layer Neural Network.ipynb|A1 Three-Layer Neural Network]] due Wednesday, Sept 8th, at 10:00 PM  | 
-| Week 3:\\ Sept 7 - Sept 11 Implementing neural networks with numpy to predict real-valued variables Deriving gradients | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/04 Scaled Conjugate Gradient.ipynb|04 Scaled Conjugate Gradient]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/05 Introduction to Gradient Descent for Neural Networks.ipynb|05 Introduction to Gradient Descent for Neural Networks]]  | |   +| Week 4:\\  Sept 14, 16  A2Autoencoders. Classification  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/06 Autoencoders.ipynb|06 Autoencoders]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/07 Introduction to Classification.ipynb|07 Introduction to Classification]]  | |  
-| Week 4:\\ Sept 14 - Sept 18    Error gradients for neural networks as matrix equationsDiscussion of A2.\\ Introduction to dashboards with python using streamlit.   [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/06 Introduction to Streamlit.ipynb|06 Introduction to Streamlit]]  [[https://www.streamlit.io/|streamlit.io]]  <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/06.1 Gradient Descent for Two-Layer Neural Networks.ipynb|06.1 Gradient Descent for Two-Layer Neural Networks]]\\ [[http://www.cs.colostate.edu/~anderson/cs545/notebooks/Handdrawn notes Sept 19.pdf|Hand drawn notes from lecture]] -->  |     +| Week 5:\\  Sept 21, 23  Convolutional neural networks.  | |  | <color red>Updated Sept. 14, 9:00 AM</color>  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A2 NeuralNetwork Class.ipynb|A2 NeuralNetwork Class]] due <color red>Monday, Sept. 20, at 10:00 PM </color>  | 
-| Week 5:\\ Sept 21 - 25    | Use of Optimizers for neural networks. Introduction to Pytorch and automatic differentation.    | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/07 Collect Weights in Vector for Optimizers.ipynb|07 Collect Weights in Vector for Optimizers]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/08 Pytorch autogradnn.Module.ipynb|08 Pytorch autogradnn.Module]]  |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A2.2 Multilayer Neural Network.ipynb|A2.2 Multilayer Neural Network]] due FridaySept 25th, at 10:00 PM.  Good examples of solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].   | +| Week 6:\\  Sept 2830  | Pytorch, JaxKeras  |
- +
  
- ===== October =====+===== October =====
  
 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 6:\\ Sept 28 - Oct 2   | Neural Network class.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/09 Initial Steps towards Defining a NeuralNetwork Class.ipynb|09 Initial Steps towards Defining a NeuralNetwork Class]] <!-- \\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/10 Classification with Linear Logistic Regression.ipynb|10 Classification with Linear Logistic Regression]] -->  +| Week 7:\\  Oct 5, 7  Reinforcement Learning  | 
-| Week 7:\\ Oct 5 - Oct 9\\ <color red>Oct 8 Lecture will not meetbut recording will be available.</color>   Help with A3. Dimensionality reduction.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/10 Help with A3.ipynb|10 Help with A3]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/11 Low-Dimensional Representations of Data.ipynb|11 Low-Dimensional Representations of Data]]   | <!-- [[https://www.wired.com/story/ai-pioneer-algorithms-understand-why/|Paper on need for causality]] -->   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A3.3 Neural Network Class.ipynb|A3.3 Neural Network Class]] due Monday, Oct 12, 10:00 PM\\ Examples of good solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].+| Week 8:\\  Oct 12, 14  Reinforcement Learning  | 
-| Week 8:\\ Oct 12 - Oct 16    | Brief overview of notes 11.\\ Introduction to Classification  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 Classification with Neural Networks.ipynb|12 Classification with Neural Networks]]\\  <!--[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 Multilabel Classification.ipynb|12 Multilabel Classification]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13.1 Pytorch nn Module.ipynb|13.1 Pytorch nn Module]] --> |  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A3.4 Classification.ipynb|A3.4 Classification]] due Wednesday, Oct 16th, at 10:00 PM --> +| Week 9:\\  Oct 19, 21  Reinforcement Learning  | 
-| Week 9:\\ Oct 19 - Oct 23    | Convolutional neural networks in numpy.  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13 NeuralNetwork_Pytorch.ipynb|13 NeuralNetwork_Pytorch]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/14 Introduction to Convolution.ipynb|14 Introduction to Convolution]] <!-- \\  <!--[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/14 NeuralNetwork_Convolutional and CIFAR-10.ipynb|14 NeuralNetwork_Convolutional and CIFAR-10]]\\  -->  |  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Project Proposal.ipynb|Project proposal]] due at 10 pm Wednesday evening, October 21st. --> +| Week 10:\\  Oct 26, 28  Brain-Computer Interfaces  |
-| Week 10:\\ Oct 26 - Oct 30   | Fully-connected and Convolutional Neural Nets in Pytorch  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/15 Convolutional Neural Networks.ipynb|15 Convolutional Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/16.1 Convolutional Neural Networks in Pytorch.ipynb|16.1 Convolutional Neural Networks in Pytorch]]  <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/16 Reinforcement Learning with Neural Network as Q Function.ipynb|16 Reinforcement Learning with Neural Network as Q Function]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/17 Reinforcement Learning to Control a Marble.ipynb|17 Reinforcement Learning to Control a Marble]]  -->  |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A4.1 Neural Network Classifier.ipynb|A4.1 Neural Network Classifier]] due Tuesday Oct 27, at 10:00 PM\\ Good examples of solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].  |+
  
 ===== November ===== ===== November =====
Line 50: Line 53:
 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 11:\\ Nov 2 - Nov 6  Comparing network performance. Introduction to Reinforcement Learning. Deep Reinforcement Learning  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/17 Partitioning Data to Compare Neural Network Performance.ipynb|17 Partitioning Data to Compare Neural Network Performance]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/18 Introduction to Reinforcement Learning.ipynb|18 Introduction to Reinforcement Learning]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/19 Reinforcement Learning with Neural Network as Q Function.ipynb|19 Reinforcement Learning with Neural Network as Q Function]]   | [[http://incompleteideas.net/book/the-book.html|Reinforcement Learning: An Introduction]], by Richard Sutton and Andrew Barto, 2nd edition  |    |  +| Week 11:\\  Nov 2, 4  Brain-Computer Interfaces  | 
-| Week 12:\\ November - 13    | Deep reinforcement learning on simulated physical control problem.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/20 Reinforcement Learning to Control a Marble.ipynb|20 Reinforcement Learning to Control a Marble]]  <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/18 Embedding With Conv1d.ipynb|18 Embedding With Conv1d.ipynb]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/19 Embedding Network.ipynb|19 Embedding Network]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/20 Transformer Tutorial.ipynb|20 Transformer Tutorial]] -->  | <!-- [[https://towardsdatascience.com/how-to-code-the-transformer-in-pytorch-24db27c8f9ec|How to Code the Transformer in Pytorch]] by Samuel Lynn-Evans -->  |   +| Week 12:\\  Nov 9, 11  Explainable AI  | 
-| Week 13:\\ Nov 16 - Nov 20      |  |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A5 Neural Networks in Pytorch.ipynb|A5 Neural Networks in Pytorch]] due WednesdayNov 18 at 10:00 PM\\  Good examples of solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].  |   +| Week 13:\\  Nov 16, 18  | Other topics  | 
-| Nov 23 - Nov 27   Fall Recess!  |  |   |+| Nov 23, 25   Fall Recess !!  | 
 +| Week 14:\\  Nov 30, Dec 2  Student Project Presentations  |
  
 ===== December ===== ===== December =====
Line 59: Line 63:
 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 14:\\ Nov 30 - Dec 4   | Clustering.\\ Support Vector Machines.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/22 K-Means Clustering, K-Nearest-Neighbor Classification.ipynb|22 K-Means Clustering, K-Nearest-Neighbor Classification]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/23 Support Vector Machines.ipynb|23 Support Vector Machines]]    | +| Week 15:\\  Dec 7, 9  Student Project Presentations  | 
-| Week 15:\\ Dec 7 - Dec 11   | Transfer learning in Reinforcement Learning. Brain-computer interfaces.  +| Dec 13-17  |  Final Exam Week  |  No Exams in this course  |
-Finals Week:\\ Dec 14 Dec 18  |   |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A6.2 Reinforcement Learning to Control a Robot.ipynb|A6.2 Reinforcement Learning to Control a Robot]]  due Tuesday, Dec 15th, 10:00 PM. Here is [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/A3mysolution.tar|A3mysolution.tar]], a neural network implementation you may choose to use for A6.\\ Good examples of solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].   |+
  
  
schedule.txt · Last modified: 2021/09/15 15:38 (external edit)