====== Schedule ======

===== Announcements =====

This is a tentative schedule.  Changes will be made as the semester progresses.

===== 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. Analysis of Trained Networks. Bottleneck Networks. Classifying Hand-Drawn Digits.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs445/notebooks/16 Introduction to Pytorch.ipynb|16 Introduction to Pytorch]]\\ [[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 =====

|< 100% 10% 20% 30% 20% 20%  >|
^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
| 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 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 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 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 =====

|< 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]]  |  | 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 13 - May 16  |  Final Exams  |  |  |  |