Syllabus
Course Info
Professor: Dr Eren Gultepe
Class Times: MW, 3:00 pm - 4:15 pm Lecture Location: Science East 2206
Office Hours: At my office, immediately after lecture or by appointment
Office: EB 3071
Email: egultep@siue.edu
Phone: (618) 650-2389
Course Description
This course focuses on the applications of the methods and techniques related to machine and statistical learning, in which the goal is to understand complex datasets.
Course Organization
Slides, labs, practice problems, group project, and deadlines will be made available through the course website. Course submissons will be performed using Blackboard. Course announcements will be made through Blackboard and email.
When a topic is being covered, first a lecture session will be held and in the following class, a lab section in which the application of the topic in the R programming language will be covered. It is highly recommended that you bring your laptop for these sessions or partner up with classmate.
The course grade is comprised of a group course project (50%) and exams (50%).
Practice homework from the textbook will be assigned but will not be graded. However, it is highly recommended you solve these problems for practice for your two exams.
Assessment Criteria
The evaluations comprising a student’s grade in the course are:
- Group Project: 50%
- Proposal (5%)
- Midpoint Report & Presentation (10%)
- Project Check-in (5%)
- Final Presentation (30%)
- Midterm: 25%
- Final Exam: 25%
Grading Scale
The grading scale (pre-curved or curve included) for the course is:
A ≥ 80%; B ≥ 65%; C ≥ 55%; D ≥ 50%
Topics Covered
The following is a tentative list of topics:
- Linear Regression
- Classification
- Resampling Methods
- Linear Model Selection and Regularization
- Moving Beyond Linearity
- Tree-Based Methods
- Support Vector Machines
- Unsupervised Learning
- Deep Learning
Late or missed assignments/exams
No makeup exams or assignments will be given. If you miss an exam or assignment, there must be documentation in writing, provided to me or to the department administrative assistant. I will still review whether you have exercised due diligence. Otherwise, you will receive 0 points for the missed exam or assignment.
Textbooks
The following textbook will be used:
- James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. New York: Springer; 2013 Jun. PDF
For more technical detail regarding the concepts covered in the course, the following textbook can be referred to:
- Hastie T, Tibshirani R, Friedman JH, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. New York: Springer; 2009 Aug. PDF
Academic Honesty
Plagiarism is the use of another person’s words or ideas without crediting that person. Plagiarism and cheating will not be tolerated and may lead to failure on an assignment, in the class, or dismissal from the University, per the SIUE academic dishonesty policy. Students are responsible for complying with University policies about academic honesty as stated in the University’s Student Academic Conduct Code.
Academic Integrity
Students are reminded that the expectations and academic standards outlined in the Student Academic Code (3C2) apply to all courses, field experiences and educational experiences at the University, regardless of modality or location. The full text of the policy can be found here: Student Academic Code.
Recordings of Class Content
Faculty recordings of lectures and/or other course materials are meant to facilitate student learning and to help facilitate a student catching up who has missed class due to illness. As such, students are reminded that the recording, as well as replicating or sharing of any course content and/or course materials without the express permission of the instructor of record, is not permitted, and may be considered a violation of the University’s Student Conduct Code (3C1), linked here: Student Conduct Code.
Accessibility
Students needing accommodations because of medical diagnosis or major life impairment will need to register with Accessible Campus Community & Equitable Student Support (ACCESS) and complete an intake process before accommodations will be given. The ACCESS office is located in the Student Success Center, Room 1270. You can also reach the office by e-mail at myaccess@siue.edu or by calling (618) 650-3726. For more information on policies, procedures, or necessary forms, please visit the ACCESS website at www.siue.edu/access.
COVID-19 Policies
- The following docunment contains SIUE’s current policies regarding classroom instruction: SIUE COVID Policies PDF
- Further information regarding SIUE’s COVID policies can be found here: Policies & Procedures
- SIUE follows the CDC’s COVID guidelines for isolation and precautions. They can be found here.
Absences
Throughout the semester, all lectures slides, course assignments, and due dates will be posted on the course website. Also, all relevant course communications will be made through email and Blackboard announcements. Thus, no Zoom links of the lectures will be provided since all relevant course material will be available online.
Since you are encouraged to work in partners for your projects, for any short unplanned absences, your partners will be a vaulable resource (e.g., for obtaining any class notes). This will ensure a students’ continued progress in the course and if needed I will to help balance the circumstances regarding the absence.
In case of accommodations requested by the ACCESS office for medical diagonsis or major life impairment, the student’s absence will be accomadated on a case-by-case basis. Please see Accessibility or Accommodations)
At the discretion of the instructor, all material, assignments, and deadlines are subject to change with prior notice. It is your responsibility to stay in touch with your instructor, review the course site regularly, or communicate with other students, to adjust as needed if assignments or due dates change.