STATISTICAL (MACHINE) LEARNING
Semester Taught - Fall
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.
- 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
Office: Room 239 Rogers Hall
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 http://www-bcf.usc.edu/~gareth/ISL/getbook.html
R and R Studio environments are available free of charge.
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)
- 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.
|Final Course Project||40%|
|(you can use software other than R for that, if necessary)|
|(problem sets, mini projects; assignments potentially graded through quizzes, TBA in advance)|
All students admitted to the University of Florida have signed a statement of academic honesty committing themselves to be honest in all academic work and understanding that failure to comply with this commitment will result in disciplinary action. This statement is a reminder to uphold your obligation as a UF student and to be honest in all work submitted and exams taken in this course and all others.
Accommodation for Students with Disabilities
Students requesting classroom accommodation must first register with the Dean of Students Office. That office will provide the student with documentation that he/she must provide to the course instructor when requesting accommodation.
Use of Library, Personal References, PC Programs and Electronic Databases
These items are university property and should be utilized with other users in mind. Never remove, mark, modify nor deface resources that do not belong to you. If you're in the habit of underlining text, do it only on your personal copy. It is inconsiderate, costly to others, and dishonest to use common references otherwise.
All faculty, staff and students of the University are required and expected to obey the laws and legal agreements governing software use. Failure to do so can lead to monetary damages and/or criminal penalties for the individual violator. Because such violations are also against University policies and rules, disciplinary action will be taken as appropriate. We, the members of the University of Florida community, pledge to hold ourselves and our peers to the highest standards of honesty and integrity.
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:
- University Counseling Center, 301 Peabody Hall, 392-1575, personal and career counseling;
- Student Mental Health, Student Health Care Center, 392-1171, personal counseling;
- Center for Sexual Assault/Abuse Recovery and Education (CARE), Student Health Care Center, 392-1161, sexual assault counseling;
- Career Resource Center, Reitz Union, 392-1601, career development assistance and counseling.