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schedule [2018/01/29 07:21]
127.0.0.1 external edit
schedule [2019/06/03 13:46] (current)
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 +====== Schedule ======
 +
 +===== Announcements =====
 +
 +Thanks, everyone, for a fun semester. ​ I very much enjoyed reading your final reports. ​ Follow [[http://​www.cs.colostate.edu/​~anderson/​cs445/​projects|this link]] to read the reports
 +
 +===== January =====
 +
 +|< 100% 10% 20% 30% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
 +| 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 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]]  |
 +
 +===== February =====
 +
 +|< 100% 10% 20% 30% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
 +| 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 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.\\ [[http://​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​good-ones/​|Examples of good solutions]] ​  |
 +| 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 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://​aclweb.org/​anthology/​D18-1472|Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks]], by Eger, Youssef, and Gurevych ​  ​| ​ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A2 Adam vs SGD.ipynb|A2 Adam vs SGD]] due Tuesday February 26, 10:00 PM.  |
 +
 +===== March =====
 +
 +|< 100% 10% 20% 30% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
 +| 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]] ​  | [[https://​towardsdatascience.com/​jupyter-lab-evolution-of-the-jupyter-notebook-5297cacde6b|Jupyter Lab: Evolution of the Jupyter Notebook]] by Parul Pandey ​ | |
 +| 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/​A3 Neural Network Regression and Activation Functions.ipynb|A3 Neural Network Regression and Activation Functions]] due Friday March 15, 10:00 PM.  |
 +|  Mar 18 - Mar 22  |  Spring Break  |
 +| Week 9:\\ Mar 25 - Mar 29 | Pytorch. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​16 Introduction to Pytorch.ipynb|16 Introduction to Pytorch]] ​ | | |
 +
 +===== April =====
 +
 +|< 100% 10% 20% 30% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
 +| Week 10:\\ Apr 1 - Apr 5  | Pytorch. Convolutional Neural Networks ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​17 Pytorch autograd, nn.Module.ipynb|17 Pytorch autograd, nn.Module]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​18 Convolutional Neural Networks.ipynb|18 Convolutional Neural Networks]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​19 Convolutional Neural Networks in Pytorch.ipynb|19 Convolutional Neural Networks in Pytorch]] ​ | [[https://​towardsdatascience.com/​getting-started-with-pytorch-part-1-understanding-how-automatic-differentiation-works-5008282073ec|Pytorch Automatic Differentiation]]\\ [[https://​www.youtube.com/​watch?​v=MswxJw-8PvE|PyTorch Autograd Explained - In-depth Tutorial]], by Elliott Waite   ​| ​ |
 +| 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/​22 Introduction to Reinforcement Learning.ipynb|22 Introduction to Reinforcement Learning]]\\ [[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]] ​ | [[http://​incompleteideas.net/​book/​the-book.html|Reinforcement Learning: An Introduction]],​ by Sutton and Barto, 2nd ed.  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​Project Proposal.ipynb|Project proposal]] due at 10 pm Friday evening. ​  |
 +| 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]] ​  ​| ​  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A4 Classifying Hand-Drawn Digits.ipynb |A4 Classifying Hand-Drawn Digits]] due Wednesday, April 17  |
 +| Week 13:\\ Apr 22 - Apr 26  | Unsupervised Learning. Dimensionality Reduction. ​ Clustering. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​26 Genetic Algorithm Search.ipynb|26 Genetic Algorithm Search]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​27 Linear Dimensionality Reduction.ipynb|27 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 =====
 +
 +|< 100% 10% 20% 30% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
 +| 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]] ​ |  |  |
 +| May 13 - May 16  |  Final Exams  |  |  | Final Project Report due Tuesday, May 14, 10:00 PM. Here are is a [[http://​www.cs.colostate.edu/​~anderson/​cs445/​projects|links to most of the project reports]] ​ |
 +
 +