User Tools

Site Tools


schedule-spring19

Table of Contents

Schedule

January

Week Topic Material Reading Assignments
Week 1:
Jan 21, 23
Overview of course, kinds of machine learning, python, jupyter notebooks, and mathematics of machine learning.
Week 2:
Jan 28, 30
Unsupervised learning. Clustering, K-Means, PCA, t-SNE.

February

Week Topic Material Reading Assignments
Week 3:
Feb 4, 6
Supervised learning. Linear and nonlinear regression with artificial neural networks. Gradient derivation and implementation. Adam, SGD. Data partitioning into train, validate and test sets.
Week 4:
Feb 11, 13
Effects of network size and other parameters. Confidence intervals using bootstrap statistics.
Week 5:
Feb 18, 20
Pytorch basics.
Week 6:
Feb 25, 27
Pytorch loss functions and optimizers.

March

Week Topic Material Reading Assignments
Week 7:
Mar 3, 5
Tensorflow and Keras.
Week 8:
Mar 10, 12
Classification with generative models. LDA and QDA.
Mar 16 - 20 Spring Break
Week 9:
Mar 24, 26
Classification, gradient derivation and implementation with Numpy.

April

Week Topic Material Reading Assignments
Week 10:
Mar 31, Apr 2
Classification with Pytorch and Keras.
Week 11:
Apr 7, 9
Introduction to reinforcement learning, with discrete state and action using tables and neural networks.
Week 12:
Apr 14, 16
Reinforcement learning with continuous state and action.
Week 13:
Apr 21, 23
Reinforcement Learning with Pytorch and Keras.
Week 14:
Apr 28, 30
Decision Trees. Random Forests.

May

Week Topic Material Reading Assignments
Week 15:
May 5, 7
Support Vector Machines. Ensembles.
May 11 - 15 Final Exam Week Final Project Report due Tuesday, May 12, 10:00 PM.
schedule-spring19.txt · Last modified: 2020/01/14 15:31 (external edit)