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start [2020/04/23 14:00]
anderson [May]
start [2020/05/08 11:30]
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-====== Schedule Spring 2020 ====== 
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-Examples of good solutions are now available at [[http://​www.cs.colostate.edu/​~anderson/​cs445/​goodSolutions|this site.]] 
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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]] ​ | 
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-===== February ===== 
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-|< 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  | Classification with generative models. LDA and QDA.  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​09 Introduction to Classification.ipynb|09 Introduction to Classification]] ​ | 
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-===== March ===== 
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-|< 100% 10% 20% 30% 20% 20%  >| 
-^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ 
-| Week 7:\\ Mar 3, 5  | Classification with linear logistic regression ​  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​10 Classification with Linear Logistic Regression.ipynb|10 Classification with Linear Logistic Regression]] ​ | [[http://​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​Sejnowski-2020.pdf|The unreasonable effectiveness of deep learning in 
-artificial intelligence]] ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A3.3 Cross-validation with Pytorch.ipynb|A3.3 Cross-validation with Pytorch]] due Thursday, March 5th, 10:​00PM ​ | 
-| Week 8:\\ Mar 10, 12  |  Class inheritance. Nonlinear logistic regression with neural nets.  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​11 Code Reuse by Class Inheritance.ipynb|11 Code Reuse by Class Inheritance]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​12.1 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|12.1 Classification with Nonlinear Logistic Regression Using Neural Networks]] ​  | 
-|  Mar 16 - 20  |  Spring Break  | 
-| Week 9:\\ Mar 24, 26  | Start of online-only lectures. Thursday this week is first online class. No new material covered, but assignment questions can be discussed. ​ | Join the Microsoft Teams meeting using your firstname.lastname@colostate.edu login. ​ | 
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-===== April ===== 
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-|< 100% 10% 20% 30% 20% 20%  >| 
-^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ 
-| Week 10:\\ Mar 31, Apr 2  | Convolutional neural networks ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​13 Convolutional Neural Networks.ipynb|13 Convolutional Neural Networks]], [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​14 Convolutional Neural Network Training with Numpy.ipynb|14 Convolutional Neural Network Training with Numpy]] ​  | |  |  
-| Week 11:\\ Apr 7, 9   | Classification with Pytorch. Assignment 5. , with discrete state and action using tables and neural networks. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​15 Convolutional Neural Networks in Pytorch.ipynb|15 Convolutional Neural Networks in Pytorch]] ​ | | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A4.2 Classification of Hand-Drawn Digits.ipynb|A4.2 Classification of Hand-Drawn Digits]] due Tuesday, April 7th, 10:​00PM ​ | 
-| Week 12:\\ Apr 14, 16  | Reinforcement learning. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​16 Introduction to Reinforcement Learning.ipynb|16 Introduction to Reinforcement Learning]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​17 Reinforcement Learning with Neural Network as Q Function.ipynb|17 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 ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​Project Proposal.ipynb|Project Proposal]] due Thursday, April 16th, 10:00PM\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A5.4 2-D and 1-D Convolutional Neural Networks in Pytorch.ipynb|A5.4 2-D and 1-D Convolutional Neural Networks in Pytorch]] due Saturday, April 18th, 10:​00PM ​ | 
-| Week 13:\\ Apr 21, 23  | Reinforcement learning. History and future of AI as discussed in [[https://​www.elementai.com/​podcast#​|episode of AI Element Podcast]] ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​18.1 Reinforcement Learning to Control a Marble.ipynb|18.1 Reinforcement Learning to Control a Marble]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​19 Reinforcement Learning for Two Player Games.ipynb|19 Reinforcement Learning for Two Player Games]] ​ |     
-| Week 14:\\ Apr 28, 30  | Decision Trees. Random Forests. Support Vector Machines. Ensembles. ​ | | |  A6  | 
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-===== May ===== 
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-|< 100% 10% 20% 30% 20% 20%  >| 
-^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ 
-| Week 15:\\ May 5, 7  | Unsupervised learning. Clustering, K-Means, PCA, t-SNE. ​ | | |  A7  | 
-| May 11 - 15  |  Final Exam Week  |  |  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​Project Report Example.ipynb|Project Report]] due Tuesday, May 12, 10:00 PM.  | 
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start.txt ยท Last modified: 2020/06/25 10:41 (external edit)