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schedule [2016/12/14 07:55]
schedule [2020/02/20 13:24] (current)
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 +====== Schedule Spring 2020 ======
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 +===== January =====
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 +|< 100% 10% 20% 30% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
 +| Week 1:\\ Jan 21, 23  | Overview of course, kinds of machine learning, python, jupyter notebooks. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​01 Course Overview.ipynb|01 Course Overview]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​01a Matrix Multiplication on GPU.ipynb|01a Matrix Multplication on GPU]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​02 Matrices and Plotting.ipynb|02 Matrices and Plotting]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​03 Linear Regression with SGD.ipynb|03 Linear Regression with SGD]]  |
 +| Week 2:\\ Jan 28, 30  | Supervised learning. Linear and nonlinear regression with artificial neural networks. ​  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​04 Linear Regression with Fixed Nonlinear Features.ipynb|04 Linear Regression with Fixed Nonlinear Features]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​05 Introduction to Neural Networks.ipynb|05 Introduction to Neural Networks]] ​ |
 +
 +===== February =====
 +
 +|< 100% 10% 20% 30% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
 +| Week 3:\\ Feb 4, 6   | Gradient descent with Adam. Effects of network size and other parameters. ​  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​05 Introduction to Neural Networks.ipynb|05 Introduction to Neural Networks]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​06.1 Gradient Descent with Adam.ipynb|06.1 Gradient Descent with Adam]] ​ |  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A1.1 Linear Regression with SGD.ipynb|A1.1 Linear Regression with SGD]] due Thursday, Feb 6th, at 10:00 PM  |
 +| Week 4:\\ Feb 11, 13  | Optimizers class. NeuralNetwork class. Partioning data.  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​07.2 Optimizers, Data Partitioning,​ Finding Good Parameters.ipynb|07.2 Optimizers, Data Partitioning,​ Finding Good Parameters]] | [[https://​ai.facebook.com/​blog/​hiplot-high-dimensional-interactive-plots-made-easy?​utm_source=Deep%20Learning%20Weekly&​utm_campaign=23123dfe9c-EMAIL_CAMPAIGN_2019_04_24_03_18_COPY_01&​utm_medium=email&​utm_term=0_384567b42d-23123dfe9c-72953565|HiPlot]] a new plotting library for visualizing results from multiple training experiments ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​Exercises1.ipynb|Exercises1]]. Do not check-in. ​ Exercises will not be graded. ​ |
 +| Week 5:\\ Feb 18, 20  | Pytorch basics, loss functions, optimizers and nn module. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​08 Pytorch autograd, nn.Module.ipynb|08 Pytorch autograd, nn.Module]] | [[https://​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​LaProp.pdf|LaProp optimizer]] ​ |[[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A2.5 Multilayer Neural Networks for Nonlinear Regression.ipynb|A2.5 Multilayer Neural Networks for Nonlinear Regression]] due Thursday, Feb 20th 10:00 PM   |
 +| Week 6:\\ Feb 25, 27  | Tensorflow and Keras. ​ |
 +
 +===== March =====
 +
 +|< 100% 10% 20% 30% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
 +| Week 7:\\ Mar 3, 5  | Classification with generative models. LDA and QDA.  |  ​
 +| Week 8:\\ Mar 10, 12  | Classification,​ gradient derivation and implementation with Numpy. ​ |
 +|  Mar 16 - 20  |  Spring Break  |
 +| Week 9:\\ Mar 24, 26  | Classification with Pytorch and Keras. ​ |
 +
 +===== April =====
 +
 +|< 100% 10% 20% 30% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
 +| Week 10:\\ Mar 31, Apr 2  | Introduction to reinforcement learning, with discrete state and action using tables and neural networks. ​ |
 +| Week 11:\\ Apr 7, 9   | Reinforcement learning with continuous state and action. ​ | 
 +| Week 12:\\ Apr 14, 16  | Reinforcement Learning with Pytorch and Keras. ​ |
 +| Week 13:\\ Apr 21, 23  | Decision Trees. Random Forests. ​ |
 +| Week 14:\\ Apr 28, 30  | Support Vector Machines. Ensembles. ​ |
 +
 +===== May =====
 +
 +|< 100% 10% 20% 30% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
 +| Week 15:\\ May 5, 7  | Unsupervised learning. Clustering, K-Means, PCA, t-SNE. ​ |
 +| May 11 - 15  |  Final Exam Week  |  |  | Final Project Report due Tuesday, May 12, 10:00 PM.  |
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