Tentative Schedule: Slides, Readings, References

·     (DM) Data Mining: Concepts and Techniques, 3^rd edition, by Jiawei Han, Micheline Kamber, and Jian Pei. Morgan Kaufmann, 2011

·     (IR) Introduction to information retrieval, by Christopher Manning, Prabhakar Raghavan, and Hinrich Schutze. Cambridge University Press, 2008 https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf

·     (PY) Introduction to Machine Learning with Python: A Guide for Data Scientists, by Andreas C. Muller and Sarah Guido. O’Reilly 2016

 

 

Date	Slides	Chapters	additional materials
Week 1	Syllabus and Introduction	DM Chapter 1,2
	Preparation, python tutorial, jupyter tutorial
Week2	Data acquisition, web scraping
	Data labeling
Week3	linear algebra, probability, and statistics review		linear algebra quick review, probability quick review, statistics basics
Week4	numpy and pandas tutorials. linear algebra examples, pandas examples with simple datasets : sample datasets
	Data cleaning	DM Chapter 3.1, 3.2, IR 2.1, 2.2	house sales data, and data cleaning notebook
Week 5	Data transformations	IR 6.2,6.3,6.4
week 5, 6	Feature engineering		Project 1 announced,
week 6	Overview of machine learning models	DM 8.1
Week 7	Linear models		SGD animation, linear regression SGD code
Week 8	Neural networks	DM 7.5	perceptron code, softmax regression code
Week 9	Model evaluation	DM8.5	Project 2 announced
Week 10	Decision tree models	DM 8.2, 8.6
Week 11	ensemble methods, Model validation
Week 12	feature selection	IR 13.5	decision tree exercise
week 12	dimensionality reduction
	Clustering	DM 10.1-10.3	pca and tsne demo, kmeans demo
	frequent itemsets and rule mining	DM 6.1, 6.2