Course Announcement

Time: Monday, Wednesday, Friday at 9:00 AM - 9:50 AM
Location (in person and on campus): Burnham Hall (828 S Halsted St, Chicago, IL 60607), Room B6

Instructor: Jie Yang
Office: SEO 513
Phone: (312) 413-3748
E-Mail: jyang06 AT uic DOT edu
Office Hours: Monday, Wednesday, Friday at 10:00 a.m. - 10:50 a.m. at UIC Zoom or by appointment

Textbook (required): Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, Springer, 2017.

Reference Books (optional):

• Bradley Efron, Trevor Hastie,  Computer Age Statistical Inference: Algorithms, Evidence and Data Science, 2016.
  
• J. D. Gibbons, S. Chakraborti,  Nonparametric Statistical Inference, 5th edition, 2011.
  
• Sharon L. Lohr,  Sampling: Design and Analysis, 3rd edition, 2022.
  
• Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani,  An Introduction to Statistical Learning: with Applications in R, 2nd edition, Springer, 2023.
  
• Sanford Weisberg,  Applied Linear Regression, 4th edition, Wiley, 2013.
  
• Max Bramer,  Principles of Data Mining, 4th edition, Springer, 2020.
  

Attendance/Participation Policy: Students are expected to attend the lectures and participate in the discussions. Attendance will be counted at least six times during the course period and the students who present may receive half credit point each time and up to three extra credit points in total on the final grade at a 100-point scale. Students who actively participate in the discussion may receive half or one credit point each time and up to five credit points in total. If for any reason you could not present in class on time, please send me an email at your earliest convenience. If you need special accommodations due to disabilities, please contact the Disability Resource Center for a Letter of Accommodation (LOA).
Assignments, Due Dates, and Deadlines: Homework will be assigned at a weekly basis; turn in your homework every Wednesday before class via UIC Blackboard;   half of the grade counts for completeness;  half of the grade counts for correctness of one selected problem.
Policy for Missed or Late Homework: Students may request up to two days' extension for each homework; late homework without request ahead of time or longer than two days will not be accepted; the lowest one homework score will be dropped for final grade.
Exam: March 6th, 2026 (Friday), 9:00 a.m. - 9:50 a.m.
Project: Students are required to work in groups on course projects and submit their final reports before May 1st, 2026, Friday, 9:00am. Each group should consist of at most three students. The projects may come from the optional problems assigned by the instructor or be proposed by the students themselves upon the approval of the instructor.
Grading: Homework 30%, Exam 30%, Project 40%
Grading Scale: 90% A , 80% B , 70% C , 60% D

Format of Exam: The exam is based on the homework and the examples discussed in class. The last class session before the exam is a review session. Please prepare any questions that you may have.

No makeup exam will be given without a valid excuse.

Course Syllabus

Homework

• Homework #1, due 01/21/2026
  

Using R (Required and free to download)

• Download R for Free -- the most popular software used by statisticians
  
• Learn R in 15 Minutes
  
• Use R to Compute Numerical Integrals
  
• RStudio -- a convenient set of integrated tools for R, including programming, plotting, and workspace management
  
• Downloadable Books on R:
  • An Introduction to R, by William N. Venables, David M. Smith and the R Development Core Team
    
  • Using R for Data Analysis and Graphics - Introduction, Code and Commentary, by John H. Maindonald
    

    More R Books in Different Languages ...

  
  
• R Code for the Course (Required):
  • Introduction to R
    
  • Useful R Tips
    
  • R code for §3.2.1 in the ESL book, including linear models for prostate cancer data
    
  • R code for §3.3 in the ESL book, including subset selection
    
  • R code for §3.4 in the ESL book, including Ridge, LASSO, Lars
    
  • R code for §3.5 in the ESL book, including PCA, PCR, PLS
    
  • R code for §3.8.4 in the ESL book, including Grouped Lasso
    
  • R code for §7.5 and §7.7 in the ESL book, including AIC, BIC
    
  • R code for §4.3 and §4.4 in the ESL book, including LDA, QDA, Logistic regression for vowel recognition data
    
  • R code for Multinomial logistic regression with trauma clinical trial data
    
  • R code for §11.3 and §11.4 in the ESL book, including Neural Network
  • R code for §12.2 and §12.3 in the ESL book, including Support vector classifier and support vector machines
  • R code for §13.3 in the ESL book, including k-Nearest-neighbor classifier
    

Relevant Course Materials

• R package for Textbook
  

Community Agreement/Classroom Conduct Policy

• Be present by turning off cell phones and removing yourself from other distractions.
  
• Be respectful of the learning space and community. For example, no side conversations or unnecessary disruptions.
  
• Use preferred names and gender pronouns.
  
• Assume goodwill in all interactions, even in disagreement.
  
• Facilitate dialogue and value the free and safe exchange of ideas.
  
• Try not to make assumptions, have an open mind, seek to understand, and not judge.
  
• Approach discussion, challenges, and different perspectives as an opportunity to think out loud, learn something new, and understand the concepts or experiences that guide other people's thinking.
  
• Debate the concepts, not the person.
  
• Be gracious and open to change when your ideas, arguments, or positions do not work or are proven wrong.
  
• Be willing to work together and share helpful study strategies.
  
• Be mindful of one another's privacy, and do not invite outsiders into our classroom.

Disability Accommodations Statement

Use of Generative Artificial Intelligence