COMS W4995 Applied Machine Learning Spring 2019 - Schedule

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#	Date	Topic	Reading	Comments
1	Wed 01/23/19	Introduction	IMLP Ch 1, APM Ch 1-2
2	Mon 01/28/19	git, GitHub and testing	Git - the simple guide, git for ages 4 and up	HW 1 posted
3	Wed 01/30/19	matplotlib and visualization	Fundamentals of Data Visualization, Systematising Glyph Design for Visualization (Chapter 2)
4	Mon 02/04/19	Introduction to supervised learning	IMLP Ch2.1-2.3.2, APM Ch 4-4.3, IMLP Ch 5.1, 5.2, APM Ch 4.4-4.8
5	Wed 02/06/19	Preprocessing	IMLP Ch 3.3, IMLP Ch 4.1-4.6, APM Ch 3,	HW 1 due, HW 2 posted
6	Mon 02/11/19	Linear models for Regression	IMLP p45-68, APM Ch 6
7	Wed 02/13/19	Linear models for Classification, SVMs
8	Mon 02/18/19	Trees, Forests & Ensembles	IMLP 2.3.5, 2.3.6, APM Ch 14.1-14.4
9	Wed 02/20/19	Gradient Boosting, Calibration	IMLP 2.3.6, APM Ch 14.5	HW 2 due
10	Mon 02/25/19	Model Evaluation	IMLP 5.3, APM Ch 16	HW 3 posted
	Wed 02/27/19	No class
11	Mon 03/04/19	Learning with Imbalanced Data	APM Ch16, SMOTE, Easy Ensembles
12	Wed 03/06/19	Model Interpretration and Feature Selection	Interpretable Machine Learning, Beware Default Random Forest Importances
13	Mon 03/11/19	Parameter tuning and Automatic Machine Learning	AutoML book (chapter 1 gives a great intro), NeurIPS tutorial video
	Wed 03/13/19	Midterm
	Mon 03/18/19	Spring break
	Wed 03/20/19	Spring break
14	Mon 03/25/19	Dimensionality Reduction	IMLP Ch 3.4.1, 3.4.3, APM p35-40
15	Wed 03/27/19	Clustering and mixture models	IMLP 3.5	HW 3 due
16	Mon 04/01/19	NMF and Outlier Detection	IMLP 3.4.2
17	Wed 04/03/19	Working with text data	IMLP Ch 7.1-7.8
18	Mon 04/08/19	Topic models for text data	IMLP Ch 7.9, Tim Hopper, Understanding Topic Models	HW 4 posted
19	Wed 04/10/19	Word and document embeddings	Mikolov 2013a, Mikolov 2013b, gensim word2vec
20	Mon 04/15/19	Neural Networks	IMLP Ch 2.3.8, DL Ch 6, Ch 7.8
21	Wed 04/17/19	Keras and Convolutional Neural Nets	DL Ch 7.12, Ch 9, keras docs, Stanford CNN course notes, Module 2, Feature Visualization	HW 4 due, HW 5 posted
22	Mon 04/22/19	Advanced Neural Networks
23	Wed 04/24/19	Time series data
24	Mon 04/29/19	Summary and Recap
25	Wed 05/01/19	Recommender systems taught by Nicolas Hug		HW 5 due
	Mon 05/06/19	Second Exam

IMLP: Mueller, Guido - Introduction to machine learning with python
APM: Kuhn, Johnson - Applied predictive modeling
DL: Goodfellow, Bengio, Courville - Deep Learning