IMLP: Mueller, Guido - Introduction to machine learning with python
APM: Kuhn, Johnson - Applied predictive modeling
DL: Goodfellow, Bengio, Courville - Deep Learning
Schedule
Week | Date | Topic | Reading | Comments |
---|---|---|---|---|
1 | 01/18/17 | Introduction; What can machine learning do for you? | IMLP Ch 1, APM Ch 1-2 | |
2 | 01/23/17 | Python, git, github, testing, CI, documentation | IMLP Ch 1, git video | Homework 1 posted |
01/25/17 | matplotlib and visualization | colormap talk | ||
3 | 01/30/17 | Introduction to supervised learning, basic model selection | IMLP p25-44, APM Ch 4-4.3, IMLP p251-262, APM Ch 4.4-4.8 | |
02/01/17 | Linear models for Regression | IMLP p45-68, APM Ch 6 | ||
4 | 02/06/17 | Linear models for Classification | IMLP p45-68, Ch 12.1-12.2, 12.5 | |
02/08/17 | Preprocessing and feature engineering | IMLP p132-140, IMLP p211-220, APM Ch 3 | Homework 1 due | |
5 | 02/13/17 | Imputation and Feature Selection | IMLP p236-241, APM Ch 19 | Homework 2 posted |
02/15/17 | Support Vector Machines for Classification and Regression | IMLP p92-103, APM Ch 13.4 | ||
6 | 02/20/17 | Decision Trees and Random Forests | IMLP p70-88, APM Ch 14.1-14.4 | |
02/22/17 | Gradient Boosting, Calibration | IMLP p89-92, APM Ch 14.5 | ||
7 | 02/27/17 | Model evaluation and imbalanced datasets | IMLP p275-302, APM Ch 16 | Homework 2 due |
03/01/17 | Case Studies (bring laptop) | |||
8 | 03/06/17 | PCA, Discriminant Analysis, Manifold Learning | IMLP p140-156, p163-168, APM p35-40 | |
03/08/17 | Midterm | |||
9 | 03/13/17 | Spring break | ||
03/15/17 | Spring break | |||
10 | 03/20/17 | Resampling strategies for Imbalanced Data |
APM Ch16, SMOTE, Easy Ensembles | Homework 3 posted |
03/22/17 | Clustering and mixture models | IMLP p168-208 | ||
11 | 03/27/17 | Cluster evaluation | ||
03/29/17 | NMF and Outlier Detection | IMLP p140-168 | ||
12 | 04/03/17 | Working with text data | IMLP p323-336 | |
04/05/17 | Topic models for text data | IMLP p347-355 | Homework 3 due, Homework 4 posted | |
13 | 04/10/17 | Word and document embeddings | Mikolov 2013a, Mikolov 2013b, gensim word2vec | |
04/12/17 | Neural Networks | IMLP p104-109, DL Ch 6, Ch 7.8 | ||
14 | 04/17/17 | Convolutional neural networks for image classification | DL Ch 7.12, Ch 9, keras docs | Homework 4 due, Homework 5 posted |
04/19/17 | Even more neural networks | DL Ch 9Stanford CNN course notes, Module 2 | ||
15 | 04/24/17 | Time series data | ||
04/26/17 | Machine Learning in Production, HW 4 | HW 4 solutionIMLP Ch 8, HICC | Homework 5 due [MOVED TO SATURDAY 04/29/17 4pm] | |
16 | 05/01/17 | Second Exam |