MATH 218: 2017 Spring
- Instructor: Albert Y. Kim - Assistant Professor of Statistics
Email: firstname.lastname@example.orgSlack team: midd-stat-learning.slack.com
- I will respond to
emailsSlack messages within 24h, but not during weekends.
- Please only
- I will respond to
- Class Location/Time:
- MWF 9:05-9:55 in Warner 507.
- You do not need to inform me of absences. Please consult your peers for what you missed.
- Office Hours: Warner 310 or the math lounge just outside.
- M 1:00-4:00 and W 1:00-4:00 (concurrent with MATH 116 office hours)
- Or by appointment
Course Description and Objectives
This course is an introduction to modern statistical, machine learning, and computational methods to analyze large and complex data sets that arise in a variety of fields, from biology to economics to astrophysics. The theoretical underpinnings of the most important modeling and predictive methods will be covered, including regression, classification, clustering, resampling, and tree-based methods. Student work will involve implementation of these concepts using open-source computational tools.
Subject to change:
An Introduction to Statistical Learning with Applications in R (ISLR) by James, Witten, Hastie, and Tibshirani. A PDF of the text can be found on the book’s homepage.
2) Computing and Software
We will chiefly be using R via the RStudio integrated development environment (IDE). Please see Getting Started for all the software and accounts we will be using.
There are four components to your final grade: problem sets, 3 midterms, engagement, and the final project.
1) Problem Sets 10%
The weekly problem sets in this class should be viewed as low-stakes opportunities to develop one’s machine learning toolbox and receive feedback on the progress of one’s learning, instead of evaluative tools used by the instructor to assign grades. To reinforce this thinking, each homework is worth only a nominal portion of the final grade. However, not making an honest effort on the homeworks will ultimately hurt you for your (individual) final project.
Collaboration on the homeworks is highly encouraged as in many situations learning is best done in groups. However you must submit your own answers and not simple rewordings of another’s work. Furthermore, all collaborations must be explicitly acknowledged at the top of your submissions.
- Assigned/due on Fridays.
- Lowest two scores dropped.
- No email submissions will be accepted; ask a classmate to print it for you.
- No extensions for any problem sets will be granted.
2) 3 Midterms 45%
- Midterm dates: Wed 3/15 (in-class), Wed 4/19 (evening), and during finals week Fri 5/19 9am-12pm in Warner 507.
- There is no extra-credit work to improve midterm scores after the fact.
- There will be no make-up nor rescheduled midterms, except in the following
cases if documentation is provided:
- serious illness or death in the family.
- athletic commitments or religious obligations if and prior notice is given. In such cases, rescheduled exams must be taken before the rest of the class.
3) Engagement 10%
It is difficult to explicit codify what constitutes “an engaged student”, so instead I present the following rough principle I will follow: you’ll only get out of this class as much as you put in. Some examples of behavior counter to this principle:
- Merely attending lectures and not participating in discussions.
- Leveraging previous experience in other settings to coast through this course.
- Not coming to office hours when the situation warrants it.
- Submitting homework that has code or content that is copied from (or only slightly modified versions of) your peers’ work, going against the philosophy of the homeworks being opportunities for practice and feedback, rather than as items to be graded on.
4) Final Project 35%
Much of this course is a build up to the final project, which is a capstone experience synthesizing everything you’ve learned over the course of the semester. You will be participating in a Kaggle (rhymes with haggle) competition: https://www.kaggle.com/competitions.
Academic Accommodations for Disabilities
Students with documented disabilities who believe that they may need accommodations in this class are encouraged to contact me as early in the semester as possible to ensure that such accommodations are implemented in a timely fashion. Assistance is available to eligible students through Student Accessibility Services. Please contact Jodi Litchfield, the ADA coordinator, at email@example.com or 802.443.5936 for more information. All discussions will remain confidential.