Date	Lecture	Info
Introduction and basics
Week 1 (1/9-1/11)	Introduction and overview [slides]
Week 2 (1/16-1/18)	Python and data science basics [Python notebook] [Numpy notebook]	References: Whirlwind Tour of Python Python Data Science Handbook Chapter 2
Data
Week 3 (1/23-1/25)	Data collection and processing [slides] [Pandas notebook]	References: Python Data Science Handbook Chapter 3
Week 4 (1/30-2/1)	Data visualization [Visualization notebook]	References: Python Data Science Handbook Chapter 4
Learning
Week 5 (2/6-2/8)	Probabilistic learning foundation	References: Introduction to Statistical Learning (Python) Chapter 2
Week 6 (2/13-2/15)	Regression [Regression notebook]	References: Introduction to Statistical Learning (Python) Chapter 3
Week 7 (2/20-2/22)	Classification [Classification notebook]	References: Introduction to Statistical Learning (Python) Chapter 4
Week 8 (2/27-29)	Unsupervised learning [Unsupervised learning notebook]	References: Introduction to Statistical Learning (Python) Chapter 12
2/29	Midterm exam
3/4-3/8	Spring Break
Week 9 (3/12-3/14)	Trees and boosting [Trees and boosting notebook]	References: Introduction to Statistical Learning (Python) Chapter 8
Week 10 (3/19-3/21)	Validation [Validation notebook]	References: Introduction to Statistical Learning (Python) Chapter 5
Applications
Week 11 (3/26-3/28)	Vision
Week 12 (4/2-4/4)	Language
Week 13 (4/9-4/11)	Recommendation systems
Week 14 (4/16-4/18)	Real world-ready development
Week 15 (4/23-4/25)	Project presentation
5/7	Final exam