schedule

# Differences

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 schedule [2016/12/14 07:50] schedule [2020/01/27 21:02] (current) Line 1: Line 1: + ====== Schedule Spring 2020 ====== + + + ===== January ===== + + |< 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. Gradient derivation and implementation. Adam, SGD. Data partitioning into train, validate and test sets.   | + + ===== February ===== + + |< 100% 10% 20% 30% 20% 20%  >| + ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ + | Week 3:\\ Feb 4, 6   | Effects of network size and other parameters. ​ Confidence intervals using bootstrap statistics. ​ |  |  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A1 Linear Regression with SGD.ipynb|A1 Linear Regression with SGD]] due Thursday, Feb 6th, at 10:00 PM  | + | Week 4:\\ Feb 11, 13  | Pytorch basics. ​ | + | Week 5:\\ Feb 18, 20  | Pytorch loss functions and optimizers. ​ | + | 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.  | + +