INST 414

Logistics

Location	Kim 1200
Time	Tue/Thu 11:00-12:15
Mailing List	https://piazza.com/umd/spring2018/inst414/home
Required Text	Think Stats
Syllabus	https://docs.google.com/document/d/1YEd5ZK0G-OzLUwK1LardjR8FNciwNPRXYQ9oQz112tA/edit?usp=sharing
Grades and Submission	ELMS

People

Professor

Jordan Boyd-Graber
ECCS 111B
Office Hours (AVW 3155): Starting 30. January, Tuesday 13:00 - 14:00 and by appointment

Course Staff

Snehesh Shrestha (Office Hours: Wed 12:00-13:00, HBK 4115)
Ananya Mukherjee (Office Hours: Thu 16:30-17:30, HBK 4115)

Schedule

How to read this table:

Do the reading under the corresponding date.
Homeworks are due at 11:55 PM (Eastern) the day listed on the schedule.

Date	Topic	Assignment Due	Materials
Thu 25. Jan	Insights from Data, Course Introduction, Python Hello World		[PDF A B C D] [Video A B C D] [Classroom]
	Readings: TS 1.1-1.2 Python (optional)
Tue 30. Jan	Python Review, Lab		[PDF A B C D] [Video A B C D] [Lab] [DATA] [Classroom]
	Readings: Python Bring laptop to class WITH PYTHON 3 INSTALLED (or pair up with someone) Functional Programming in Python (optional, good review if you haven't seen my Python style before): Functional Programming in Python Book Functional Programming in Python Tutorial Functional Programming in Python Blog Post
Thu 1. Feb	HW 0 Lab		[Video] [Classroom]
	Readings: TS 1.3-1.8
Mon 5. Feb	Homework 0 Due: Kaggle Exploration	[Kaggle]
Tue 6. Feb	Probability Refresher: Definitions, Notation		[PDF A B C D] [Video AB C D] [Practice] [Classroom]
	Readings: Grinstead and Snell, Chapters 1-2 (Optional) Sharon's Review
Thu 8. Feb	Conditional Probability		[PDF A B] [Video A B] [Practice] [Classroom]
	Readings: TS 2 Grinstead and Snell, Chapter 4
Tue 13. Feb	HW 1 Lab		[Classroom]
Thu 15. Feb	Discrete Distributions		[PDF A B C] [Video A B C] [Practice] [Classroom]
	Readings: Grinstead and Snell, 5.1 TS 3
Thu 15. Feb	Homework 1 Due: Data Wrangling	[Github Kaggle]
Tue 20. Feb	Visualization		[PDF A B C] [Video A B C] [Classroom]
Thu 22. Feb	HW 2 Lab		[PDF] [Classroom]
Tue 27. Feb	Continuous Distributions		[PDF A B C] [Video A B C] [Practice] [Classroom]
	Readings: Grinstead and Snell, 5.2 TS 4
Thu 1. Mar	Naive Bayes		[Video] [PDF A B] [Classroom]
	Readings: Naive (Bayes) at Forty
Thu 1. Mar	Homework 2 Due: Tell a Story with Data	[Github]
Tue 6. Mar	Decision Trees		[Video] [PDF] [Classroom]
	Readings: Decision Trees in Python
Thu 8. Mar	Linear Regression		[Regression] [PDF A B] [Classroom]
	Readings: TS 10
Tue 13. Mar	Midterm Review		[Classroom]
Thu 15. Mar	Midterm
Tue 27. Mar	Class Cancelled		[Video A]
Thu 29. Mar	Logistic Regression		[Video A Class] [PDF A]
	Readings: TS 11
Tue 3. April	Support Vector Machines		[PDF A B C] [Video Class] [Example]
	Readings: Support Vector Machines
Thu 5. Apr	Feature Engineering		[Viedo A B C D] [PDF A B C D Class] [Code] [Data]
Tue 10. Apr	Clustering		[PDF A B C] [Video A B C Class]
	Readings: CIML chapter 15.
Thu 12. Apr	Project Brainstorming
Tue 17. Apr	HW3 Lab		[Class]
Thu 19. Apr	Topic Models		[PDF A B] [Video A B C Class]
	Readings: Chapter 1 of Applications of Topic Models Optional: Gibbs Sampling for the Uninitiated
Thu 19. Apr	Homework 3 Due: Feature Engineering	[Github]
Thu 19. Apr	Project Proposal Due	[Github]
Tue 24. Apr	Ensemble Methods		[Video Class] [PDF A B C ]
	Readings: The Boosting Approach
Thu 26. Apr	Deep Learning		[Video A B Class] [PDF A B C]
	Readings: A Gentle Introduction to Deep Learning
Tue 1. May	Project Workshop		[Class]
Thu 3. May	Social Implications		[Video Class]
	Readings: Seven Deadly Sins of Predicting the Future of AI
Tue 8. May	Project Presentations I		[Video: Pres Review]
Thu 10. May	Project Presentations II / Final Review		[PDF]
Sat 12. May, 8:00 (Yuck!)	Final Exam

Data Science

Logistics

People

Professor

Course Staff

Schedule