/***

===== August =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
| Week 1:\\  Aug 24 - Aug 28    | Overview of course and the machine learning field. Reminder 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  |


===== September =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
| Week 2:\\ Aug 31 - Sept 4    | Help with A1. Review of gradients. Gradient 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 - 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 - Sept 18    | Error gradients for neural networks as matrix equations. Discussion 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 - 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 autograd, nn.Module.ipynb|08 Pytorch autograd, nn.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 Friday, Sept 25th, at 10:00 PM.  Good examples of solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].   |



 ===== October =====

|< 100% 18% 20% 22% 20% 20%  >|
^  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 - Oct 9\\ <color red>Oct 8 Lecture will not meet, but 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 - 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 - 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 - 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 =====

|< 100% 18% 20% 22% 20% 20%  >|
^  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 12:\\ November 9 - 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 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 Wednesday, Nov 18 at 10:00 PM\\  Good examples of solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].  |   |
| Nov 23 - Nov 27  |  Fall Recess!  |  |   |

===== December =====

|< 100% 18% 20% 22% 20% 20%  >|
^  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 - Dec 11   | Transfer learning in Reinforcement Learning. Brain-computer interfaces.  |  |
| 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]].   |


***/