Location and Time

• Location: 223 Gregory Hall
• Time: MWF, 10:00 AM - 10:50 AM

Course Staff

Course Objectives

After this course, students should be expected to be able to …

• identify supervised (regression and classification) and unsupervised (clustering) learning problems.
• decide effective methods to use in solving various learning problem.
• understand the fundamental theory behind statistical learning methods.
• implement the statistical learning methods in practice using a statistical computing environment.

Course Content

Tentative subjects include:

• Basics: Supervised vs Unsupervised Learning, Bias-Variance Trade-Off, Cross-Validation
• Regression: SLR, MLR, Model Selection, Penalized Regression, Smoothing, Generalized Additive Models, Regression Trees.
• Classification: Logistic Regression, KNN, LDA, QDA, Naive Bayes, Support Vector Machines, Classification Trees, Bagging, Boosting, Random Forests
• Clustering: PCA, K-Means, Hierarchical Clustering, Mixture Models, EM Algorithm

If time permits, we will discuss the basics of neural networks, and build towards the fundamentals of deep learning.

Textbooks

• Required: ISL - An Introduction to Statistical Learning with Applications in R
  • Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
• Supplemental: ESL - The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • Trevor Hastie, Robert Tibshirani, Jerome Friedman
• Supplemental: R4SL - R for Statistical Learning
  • David Dalpiaz
• Supplemental: ASR - Applied Statistics with R
  • David Dalpiaz
• Supplemental: APM - Applied Predictive Modeling
  • Max Kuhn and Kjell Johnson
• Supplemental: ADA - Advanced Data Analysis from an Elementary Point of View
  • Cosma Rohilla Shalizi

Additional reading material will be posted on the course website.

Prerequisites

A course which covers linear regression and uses R, such as STAT 420 or STAT 425. Basic knowledge of probability and linear algebra is also assumed.

Email Policy

Due to the large size of this course, we follows a strict email policy. Before sending an email, please read this note.

Homework

There will be eight homework assignments, your best seven will count towards your grade. (Drop lowest score.) Please see this note for a detailed homework policy, including the directions for all assignments.

Quizzes

There will be two in-class quizzes. The quiz dates are:

• Quiz 1: Wednesday, March 7
• Quiz 2: Wednesday, April 25

Projects

Project due dates, assignment details, and group assignments will be announced after the midpoint of the semester.

Software

R and RStudio are required software for this course. R is a freely available language and environment for statistical computing and graphics. RStudio is a free and open-source integrated development environment for R. You must have access to a computer where you are able to install the most up-to-date versions of R and RStudio, as well as install R packages.

Course Grades

Grading Scale

Grades are not curved or adjusted. This is not to dishearten students, but to let them know that their grade is based on individual effort and not on comparative effort.

Grade Disputes

If you feel your homework or exam was graded incorrectly, you have one week from the date it was returned to discuss it with the course staff. After one week, grading is final except for exceptional circumstances. You may not simply ask for a re-grade, but instead must justify to the staff why the grading was done incorrectly. Also, by disputing any grading, you agree to allow the course staff to review the entire homework or exam for other errors missed during grading.

Attendence

You are expected to attend all lectures and discussions. Failure to do so may not have a direct effect on your course grade, but will likely have a significant indirect effect. Any known or potential extracurricular conflicts should be discussed in person with the instructor during the first week of classes, or as soon as they arise.

Academic Integrity

The official University of Illinois policy related to academic integrity can be found in Article 1, Part 4 of the Student Code. Section 1-402 in particular outlines behavior which is considered an infraction of academic integrity. These sections of the Student Code will be upheld in this course. Any violations will be dealt with in a swift, fair and strict manner. Homework assignments are meant to be learning experiences. You may discuss the exercises with other students, but you must write-up the solutions on your own. In short, do not cheat, it is not worth the risk. You are more likely to get caught than you believe. If you think you may be operating in a grey area, you most likely are.

Safety

The university values your safety. Please read this document or watch this video.

Disability Accommodations

To obtain disability-related academic adjustments or auxiliary aids, students with disabilities must contact the course instructor and the Disability Resources and Educational Services (DRES) as soon as possible. To contact DRES, you may visit 1207 S. Oak St., Champaign, call 217-333-4603, e-mail disability@illinois.edu or go to the DRES website.

Changes

The instructor reserves the right to make any changes he considers academically advisable. Such changes, if any, will be announced in class. Please note that it is your responsibility to attend the class and keep track of the proceedings.

Home