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| ====== Schedule ====== | ====== Schedule ====== | ||
| - | /* | ||
| - | Follow this link to view all [[https://echo.colostate.edu/ess/portal/section/37e | ||
| - | 115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]]. | ||
| - | */ | ||
| ===== Announcements ===== | ===== Announcements ===== | ||
| - | Lecture videos are available at this [[https://colostate.instructure.com/courses/61937/external_tools/2755|CS480 video recordings site]]. | + | This is a tentative schedule. Changes will be made as the semester progresses. |
| ===== January ===== | ===== January ===== | ||
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| |< 100% 10% 20% 30% 20% 20% >| | |< 100% 10% 20% 30% 20% 20% >| | ||
| ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ||
| - | | Week 1:\\ Jan 16 - Jan 19 | Overview. Intro to machine learning. Python. | [[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/02 Matrices and Plotting.ipynb|02 Matrices and Plotting]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/03 Linear Regression.ipynb|03 Linear Regression]] | [[http://www.labri.fr/perso/nrougier/from-python-to-numpy/|From Python to Numpy]], Chapters 1-2\\ [[http://www.deeplearningbook.org/|Deep Learning]], Chapters 1-5 | | + | | Week 1:\\ Jan 22 - Jan 25 | Overview. Intro to machine learning. Python. | [[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/01 High-D Spaces.ipynb|01 High-D Spaces]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/02 Matrices and Plotting.ipynb|02 Matrices and Plotting]] | [[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\\ [[http://www.deeplearningbook.org/|Deep Learning]], Chapters 1 - 5.1.4 | |
| - | | Week 2:\\ Jan 22 - Jan 26 | | + | | Week 2:\\ Jan 28 - Feb 1 | Fitting linear models to data as a direct matrix calculation, and incrementally using stochastic gradient descent (SGD) | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/03 Linear Regression.ipynb|03 Linear Regression]] | [[http://www.deeplearningbook.org/contents/ml.html|Deep Learning, Section 5.1.4 and 5.9]] | |
| - | | Week 3:\\ Jan 29 - Feb 2 | | + | |
| ===== February ===== | ===== February ===== | ||
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| |< 100% 10% 20% 30% 20% 20% >| | |< 100% 10% 20% 30% 20% 20% >| | ||
| ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ||
| - | | Week 4:\\ Feb 5 - Feb 9 | | + | | Week 3:\\ Feb 4 - Feb 8 | Stochastic gradient descent (SGD). Ridge regression. Data partitioning. Probabilistic Linear Regression. Regression with fixed nonlinearities. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/04 Linear Regression Using Stochastic Gradient Descent (SGD).ipynb|04 Linear Regression Using Stochastic Gradient Descent (SGD)]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/05 Linear Ridge Regression and Data Partitioning.ipynb|05 Linear Ridge Regression and Data Partitioning]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/07 Linear Regression with Fixed Nonlinear Features.ipynb|07 Linear Regression with Fixed Nonlinear Features]] |[[http://www.deeplearningbook.org/|Deep Learning]], Section 7.3\\ [[http://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?_r=0|The Great A.I. Awakening]], by Gideon Lewis-Krause, NYT, Dec 14, 2016. | | |
| - | | Week 5:\\ Feb 12 - Feb 16 | | + | | Week 4:\\ Feb 11 - Feb 15 | Introduction to nonlinear regression with neural networks. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/08 Stochastic Gradient Descent with Parameterized Activation Function.ipynb|08 Stochastic Gradient Descent with Parameterized Activation Function]] | [[http://www.deeplearningbook.org/|Deep Learning]], Chapter 6 (skip 6.2) | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/A1 Stochastic Gradient Descent for Simple Models.ipynb|A1 Stochastic Gradient Descent for Simple Models]] due Tuesday, February 12, 10:00 PM. | |
| - | | Week 6:\\ Feb 19 - Feb 23 | | + | | Week 5:\\ Feb 18 - Feb 22 | More neural networks | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/09 Scaled Conjugate Gradient for Training Neural Networks.ipynb|09 Scaled Conjugate Gradient for Training Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/10 More Nonlinear Regression with Neural Networks.ipynb|10 More Nonlinear Regression with Neural Networks]] | | |
| - | | Week 7:\\ Feb 26 - Mar 2 | | + | | Week 6:\\ Feb 25 - Mar 1 | Autoencoders. Activation functions. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/11 Autoencoder Neural Networks.ipynb|11 Autoencoder Neural Networks]] | [[https://arxiv.org/pdf/1710.05941.pdf|Searching for Activation Functions]], by Ramachandran, Zoph, and Le | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/A2 Adam vs SGD.ipynb|A2 Adam vs SGD]] due Monday February 25, 10:00 PM. | |
| ===== March ===== | ===== March ===== | ||
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| |< 100% 10% 20% 30% 20% 20% >| | |< 100% 10% 20% 30% 20% 20% >| | ||
| ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ||
| - | | Week 8:\\ Mar 5 - Mar 9 | | + | | Week 7:\\ Mar 4 - Mar 8 | Classification. LDA and QDA. K-Nearest Neighbors. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/12 Introduction to Classification.ipynb|12 Introduction to Classification]] \\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/13 Gaussian Distributions.ipynb|13 Gaussian Distributions]] | | | |
| - | | Mar 12 - Mar 16 | Spring Break | | + | | Week 8:\\ Mar 11 - Mar 15 | Classification with Neural Networks | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/14 Classification with Linear Logistic Regression.ipynb|14 Classification with Linear Logistic Regression]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/15 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|15 Classification with Nonlinear Logistic Regression Using Neural Networks]] \\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/16 Introduction to Pytorch.ipynb|16 Introduction to Pytorch]] | |
| - | | Week 9:\\ Mar 19 - Mar 23 | | + | | Mar 18 - Mar 22 | Spring Break | |
| - | | Week 10:\\ Mar 26 - Mar 30 | | + | | Week 9:\\ Mar 25 - Mar 29 | Analysis of Trained Networks. Bottleneck Networks. Classifying Hand-Drawn Digits. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/17 Analysis of Neural Network Classifiers and Bottleneck Networks.ipynb|17 Analysis of Neural Network Classifiers and Bottleneck Networks]] \\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/18 Dealing with Time Series by Time-Embedding.ipynb|18 Dealing with Time Series by Time-Embedding]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/19 Recurrent Neural Networks.ipynb|19 Recurrent Neural Networks]] | | | |
| ===== April ===== | ===== April ===== | ||
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| |< 100% 10% 20% 30% 20% 20% >| | |< 100% 10% 20% 30% 20% 20% >| | ||
| ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ||
| - | | Week 11:\\ Apr 2 - Apr 6 | | + | | Week 10:\\ Apr 1 - Apr 5 | Convolutional Neural Networks | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/20 Classifying Hand-drawn Digits.ipynb|20 Classifying Hand-drawn Digits]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/21 Convolutional Neural Networks.ipynb|21 Convolutional Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/22 Introduction to Reinforcement Learning.ipynb|22 Introduction to Reinforcement Learning]] | [[http://incompleteideas.net/book/the-book.html|Reinforcement Learning: An Introduction]], by Sutton and Barto, 2nd ed. | | |
| - | | Week 12:\\ Apr 9 - Apr 13 | | + | | Week 11:\\ Apr 8 - Apr 12 | Reinforcement Learning. Games using Tabular Q functions. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/23 Reinforcement Learning with Neural Network as Q Function.ipynb|23 Reinforcement Learning with Neural Network as Q Function]] | | [[https://drive.google.com/open?id=1KHAxeIwL3ait2ZUbILdbJjCLW47JwxKpdjsAr5kkkZk|Project proposal]] due at 10 pm Friday evening. You are welcome to start with a copy of the linked Google Doc. | |
| - | | Week 13:\\ Apr 16 - Apr 20 | | + | | Week 12:\\ Apr 15 - Apr 19 | Reinforcement Learning using Neural Networks as Q functions. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/24 Reinforcement Learning to Control a Marble.ipynb|24 Reinforcement Learning to Control a Marble]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/25 Reinforcement Learning for Two Player Games.ipynb|25 Reinforcement Learning for Two Player Games]] | |
| - | | Week 14:\\ Apr 23 - Apr 27 | | + | | Week 13:\\ Apr 22 - Apr 26 | Unsupervised Learning. Dimensionality Reduction. Clustering. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/26 Linear Dimensionality Reduction.ipynb|26 Linear Dimensionality Reduction]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/27 Examples of Linear Dimensionality Reduction.ipynb|27 Examples of Linear Dimensionality Reduction]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/28 K-Means Clustering.ipynb|28 K-Means Clustering]] | |
| + | | Week 14:\\ Apr 29 - May 3 | Hierarchical clustering. K Nearest Neighbors Classification. Support Vector Machines. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/29 Hierarchical Clustering.ipynb|29 Hierarchical Clustering]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/30 Nonparametric Classification with K Nearest Neighbors.ipynb|30 Nonparametric Classification with K Nearest Neighbors]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/31 Support Vector Machines.ipynb|31 Support Vector Machines]] | | } | ||
| ===== May ===== | ===== May ===== | ||
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| |< 100% 10% 20% 30% 20% 20% >| | |< 100% 10% 20% 30% 20% 20% >| | ||
| ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ||
| - | | Week 15:\\ Apr 30 - May 4 | | + | | Week 15:\\ May 6 - May 10 | Ensembles. Other topics. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/32 Ensembles of Convolutional Neural Networks.ipynb|32 Ensembles of Convolutional Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/33 Machine Learning for Brain-Computer Interfaces.ipynb|33 Machine Learning for Brain-Computer Interfaces]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/34 Modeling Global Climate Change.ipynb|34 Modeling Global Climate Change]] | | Final Project Report due Wednesday, May 8, 10:00 PM. Here is a [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/Project Report Example.ipynb|Project Report Example]] | |
| - | | May 7 - May 10 | Final Exams | | + | | May 13 - May 16 | Final Exams | | | | |
| - | |||
| - | |||
| - | |||
| - | /* | ||
| - | ===== January ===== | ||
| - | |||
| - | |< 100% 10% 20% 30% 20% 20% >| | ||
| - | ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ||
| - | | Week 1:\\ Jan 16 - Jan 19 | Overview. Intro to machine learning. Python. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/01 Course Overview.ipynb|01 Course Overview]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/02 Matrices and Plotting.ipynb|02 Matrices and Plotting]], | [[http://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?_r=0|The Great A.I. Awakening]], by Gideon Lewis-Krause, NYT, Dec 14, 2016.\\ Section 1 of [[http://www.scipy-lectures.org|Scipy Lecture Notes]] | | | ||
| - | | Week 2:\\ Jan 23 - Jan 27 | Probability distributions and regression. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/03 Linear Regression.ipynb|03 Linear Regression]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/04 Gaussian Distributions.ipynb|04 Gaussian Distributions]] | | | ||
| - | |||
| - | |||
| - | ===== February ===== | ||
| - | |||
| - | |< 100% 10% 20% 30% 20% 20% >| | ||
| - | ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ||
| - | | Week 3:\\ Jan 30 - Feb 3 | Probabilistic Linear Regression. Ridge regression. Data partitioning. On-line, incremental regression. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/05 Fitting Gaussians.ipynb|05 Fitting Gaussians]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/06 Probabilistic Linear Regression.ipynb|06 Probabilistic Linear Regression]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/07 Linear Ridge Regression and Data Partitioning.ipynb|07 Linear Ridge Regression and Data Partitioning]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/08 Sample-by-Sample Linear Regression.ipynb|08 Sample-by-Sample Linear Regression]] | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1 Linear Regression.ipynb|A1 Linear Regression]] due Monday, January 30th at 10:00 PM. | | ||
| - | | Week 4:\\ Feb 6 - Feb 10 | Regression with fixed nonlinearities. Nonlinear regression with neural networks.\\ Feb 10: Guest Speaker [[https://www.linkedin.com/in/mike-morain-07223710|Mike Morain]], Machine Learning at Amazon, UK. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/09 Linear Regression with Fixed Nonlinear Features.ipynb|09 Linear Regression with Fixed Nonlinear Features]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/10 Nonlinear Regression with Neural Networks.ipynb|10 Nonlinear Regression with Neural Networks]] | | | | ||
| - | | Week 5:\\ Feb 13 - Feb 17 | Neural Networks | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/10 Nonlinear Regression with Neural Networks.ipynb|10 Nonlinear Regression with Neural Networks]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/11 More Nonlinear Regression with Neural Networks.ipynb|11 More Nonlinear Regression with Neural Networks]] | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2 Ridge Regression with K-Fold Cross-Validation.ipynb|A2 Ridge Regression with K-Fold Cross-Validation]] due Monday, February 13th at 10:00 PM.\\ Here are [[A2-good-ones|examples of good A2 reports.]] | | ||
| - | | Week 6:\\ Feb 20 - Feb 24 | Neural Networks. Autoencoders. Guest lectures by our GTA, Jake Lee. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/12 Autoencoder Neural Networks.ipynb|12 Autoencoder Neural Networks]] | | | | ||
| - | | Week 7:\\ Feb 27 - Mar 3 | Recurrent Neural Networks.\\ Conditional probabilities and Bayes Rule | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/13 Recurrent Neural Networks.ipynb|13 Recurrent Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/14 Introduction to Classification.ipynb|14 Introduction to Classification]] | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3 Neural Network Regression.ipynb|A3 Neural Network Regression]] due Wednesday, March 1st at 10:00 PM.\\ Here are [[A3-good-ones|examples of good A3 reports.]] | | ||
| - | |||
| - | ===== March ===== | ||
| - | |||
| - | |< 100% 10% 20% 30% 20% 20% >| | ||
| - | ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ||
| - | | Week 8:\\ Mar 6 - Mar 10 | Classification. LDA and QDA. Linear and Nonlinear Logistic Regression. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/15 Classification with Linear Logistic Regression.ipynb|15 Classification with Linear Logistic Regression]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/16 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|16 Classification with Nonlinear Logistic Regression Using Neural Networks]] | | | | ||
| - | | Week 9:\\ Mar 20, Mar 24\\ <color red>No class March 22nd.</color> | Classification. Analysis of Trained Networks. Bottleneck Networks. Hand-Drawn Digit Classification. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/17 Analysis of Neural Network Classifiers and Bottleneck Networks.ipynb|17 Analysis of Neural Network Classifiers and Bottleneck Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/18 Digits.ipynb|18 Digits]] | | | | ||
| - | | Week 10:\\ Mar 27 - Mar 31 | Convolutional Neural Networks. Reinforcement Learning. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/19 Convolutional Neural Networks.ipynb|19 Convolutional Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/20 Introduction to Reinforcement Learning.ipynb|20 Introduction to Reinforcement Learning]] | [[http://incompleteideas.net/sutton/book/the-book-2nd.html| Reinforcement Learning: An Introduction]], by Richard Sutton and Andrew Barto. 2nd edition draft. On-line and free. | | | ||
| - | |||
| - | ===== April ===== | ||
| - | |||
| - | |< 100% 10% 20% 30% 20% 20% >| | ||
| - | ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ||
| - | | Week 11:\\ Apr 3 - Apr 7 | Reinforcement Learning. Two-player games. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/21 Reinforcement Learning for Two Player Games.ipynb|21 Reinforcement Learning for Two Player Games]] | [[http://incompleteideas.net/sutton/book/the-book-2nd.html| Reinforcement Learning: An Introduction]], by Richard Sutton and Andrew Barto. 2nd edition draft. On-line and free. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4 Classification with LDA and Logistic Regression.ipynb|A4 Classification with LDA and Logistic Regression]] due Wednesday, April 5th at 10:00 PM.\\ Here are [[A4-good-ones|examples of good A4 reports.]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Project Proposal.ipynb|Project Proposal]] due Friday, April 7th at 10:00 PM. | | ||
| - | | Week 12:\\ Apr 10 - Apr 14 | Neural networks as Q functions. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/22 Reinforcement Learning with Neural Network as Q Function.ipynb|22 Reinforcement Learning with Neural Network as Q Function]]\\ [[http://www.cs.colostate.edu/~anderson/cs480/notebooks/17pole.odp|Faster RL by Pre-training]] | [[https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/|The Dark Secret at the Heart of AI]]\\ [[https://flipboard.com/@flipboard/flip.it%2FVaiyLS-the-tiny-changes-that-can-cause-ai-to-f/f-32bef81237%2Fbbc.com|The Tiny Changes That Can Cause AI to Fail]] | | | ||
| - | | Week 13:\\ Apr 17 - Apr 21 | Unsupervised Learning. Dimensionality reduction. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/23 Linear Dimensionality Reduction.ipynb|23 Linear Dimensionality Reduction]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/24 Nonlinear Dimensionality Reduction with Digits Example.ipynb|24 Nonlinear Dimensionality Reduction with Digits Example]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/25 K-Means Clustering.ipynb|25 K-Means Clustering]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/26 Hierarchical Clustering.ipynb|26 Hierarchical Clustering]] | | | | ||
| - | | Week 14:\\ Apr 24 - Apr 28 | Nonparametric Classification Algorithms | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/27 Nonparametric Classification with K Nearest Neighbors.ipynb|27 Nonparametric Classification with K Nearest Neighbors]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/28 Support Vector Machines.ipynb|28 Support Vector Machines]] | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5 Control a Marble with Reinforcement Learning.ipynb|A5 Control a Marble with Reinforcement Learning]] due Monday, April 24th at 10:00 PM.\\ Here are [[A5-good-ones|examples of good A5 reports.]] | | ||
| - | |||
| - | ===== May ===== | ||
| - | |||
| - | |< 100% 10% 20% 30% 20% 20% >| | ||
| - | ^ Week ^ Topic ^ Material ^ Reading ^ Assignments ^ | ||
| - | | Week 15:\\ May 1 - May 5 | Brain-Computer Interfaces. Ensembles. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/29 Machine Learning for Brain-Computer Interfaces.ipynb|29 Machine Learning for Brain-Computer Interfaces]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/30 Comparison of Algorithms for BCI.ipynb|30 Comparison of Algorithms for BCI]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/31 Convolutional Neural Networks for BCI.ipynb|31 Convolutional Neural Networks for BCI]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/32 Ensembles of Convolutional Neural Networks.ipynb|32 Ensembles of Convolutional Neural Networks]]\\ [[http://www.cs.colostate.edu/~anderson/cs480/notebooks/16harvard.odp|Patterns in EEG for Brain-Computer Interfaces and Recent Results with Tripolar EEG Electrodes]] | | Please complete the Course Surveys that are now available on Canvas. Fill out the survey for your section, either on-campus or distance-learning. | | ||
| - | | Finals Week:\\ May 8 - May 11 | | | | Final project due Tuesday, May 9, 10:00 PM. [[Final Project Report|Here is a summary]] of what is expected in your reports. | | ||
| - | |||
| - | */ | ||
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