University of Florida

ABE 6933

Semester Taught - Fall

Catalog Description

Credits: 3

This course focuses on methodology and application of tools of statistical (machine) learning. In contrast with courses with similar names offered by Computer Science (CS) and Industrial Engineering (IE), it emphasizes statistical approaches to machine learning. The course prioritizes application and the intuition behind statistical methods rather than formal derivations and justification of the procedures. R will be the statistical computing environment of choice.
The course is designed to complement existing offerings in CS, IE and Statistics. The target audience is students in engineering, applied statistics/biostatistics and quantitatively minded grads in CALS/CLAS.


A recent first graduate statistical methods class (such as ALS5932 or STA6166); experience reading and writing simple computer programs in a scripting language (ideally, in R); basic undergraduate quantitative training (calculus and basic matrix/linear algebra). Stats/biostats grad students with stronger math and stats background are welcome. To keep things interesting, more advanced problem sets and project options will be offered.

Course Objectives

  • Learn the principles behind predictive modeling and model validation
  • Learn and be able to use R to apply different classes of stat learning methods
  • Learn to use R as a statistical computing environment – for statistical inference, prediction, scientific computing and data visualization


Nikolay Bliznyuk
Office: Room 239 Rogers Hall
Phone: 392-1864

Material/Supply Fees


Class Materials Required

Lecture notes and readings will be posted on course page. Course text (James et al) is available free of charge online from the authors at
R and R Studio environments are available free of charge.

Course Outline


We shall cover most topics in the course text (please check out the table of contents).
Tentative list of topics

  • Scope of Statistical Learning
  • Linear Regression (review, matrix manipulations, relation to predictive modeling)
  • Classification
  • Resampling Methods
  • Linear Model Selection and Regularization
  • Moving Beyond Linearity
  • Tree-Based Methods
  • Support Vector Machines
  • Unsupervised Learning


Attendance and participation in class is expected. Makeup tests are discouraged.

90-100 A
87-89 B+
80-86 B
77-79 C+
70-76 C
<70 D
Final Course Project 40%
(you can use software other than R for that, if necessary)
Homework 60%
(problem sets, mini projects; assignments potentially graded through quizzes, TBA in advance)

Academic Honesty

As a student at the University of Florida, you have committed yourself to uphold the Honor Code, which includes the following pledge: “We, the members of the University of Florida community, pledge to hold ourselves and our peers to the highest standards of honesty and integrity.”  You are expected to exhibit behavior consistent with this commitment to the UF academic community, and on all work submitted for credit at the University of Florida, the following pledge is either required or implied: "On my honor, I have neither given nor received unauthorized aid in doing this assignment." 
It is assumed that you will complete all work independently in each course unless the instructor provides explicit permission for you to collaborate on course tasks (e.g. assignments, papers, quizzes, exams). Furthermore, as part of your obligation to uphold the Honor Code, you should report any condition that facilitates academic misconduct to appropriate personnel. It is your individual responsibility to know and comply with all university policies and procedures regarding academic integrity and the Student Honor Code.  Violations of the Honor Code at the University of Florida will not be tolerated. Violations will be reported to the Dean of Students Office for consideration of disciplinary action. For more information regarding the Student Honor Code, please see:  

UF Counseling Services

Resources are available on-campus for students having personal problems or lacking clear career and academic goals which interfere with their academic performance. These resources include:

  1. University Counseling Center, 301 Peabody Hall, 392-1575, personal and career counseling;
  2. Student Mental Health, Student Health Care Center, 392-1171, personal counseling;
  3. Center for Sexual Assault/Abuse Recovery and Education (CARE), Student Health Care Center, 392-1161, sexual assault counseling;
  4. Career Resource Center, Reitz Union, 392-1601, career development assistance and counseling.