Class time: Tuesday and Thursday, 9:35AM - 10:55AM

Classroom: Baker Systems 198

Course website: https://sites.google.com/view/osu-cse-5523-au22-zhu

Course Description

Course abstract: Introduction to basic concepts of machine learning and statistical pattern recognition; techniques for classification, clustering, and data representation and their theoretical analysis.

Course credits: 3 units

Pre-requisites:

• Required background:

§ Linear algebra: Math 2568, 2174, 4568, or 5520H

§ Artificial intelligence: 3521, 5521, or 5243

§ Statistics and probability: 5522, Stat 3460, or 3470

• Students in the class are expected to have a decent degree of mathematical sophistication and to be familiar with linear algebra, multivariate calculus, probability, and statistics. Students are also expected to have knowledge of programming, algorithm design, and data structures.

• Programming in Python 3 is required.

• Review materials can be found: linear algebra, probability, Python-1, Python-2, Python-3

Textbook (optional)

• Kevin P. Murphy, Machine Learning: A Probabilistic Perspective. The MIT press, 2012. (Free online version, Amazon)

• Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning, 2009. (Free online version, Amazon)

• Hsuan-Tien Lin, Malik Magdon-Ismail, and Yaser Abu-Mostafa, Learning From Data, AMLBook, 2012. (Amazon)

• Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep learning. The MIT press, 2016. (Amazon)

Useful reference:

• Kaare Brandt Petersen and Michael Syskind Pedersen, The Matrix Cookbook

Grading (tentative):

• Homework: 50%

• Midterm exam: 25%

• Final exam (TBA): 25%

• The midterm and final exams may be re-distributed into three exams. The midterm or final exam may be replaced by a project. The final exam is, by default, cumulative.

Homework:

• There will be around 5-6 homework assignments.

• Each assignment will consist of exercises and python implementations.

• Programming in Python 3 is required.

• The homework will be submitted through Carmen.

• For the problem set and the report of the programming set, we may only allow pdf submission with your answers typed in LaTeX.

• You must strictly follow the homework and submission instructions.

Midterm exam & final exam:

• The midterm and final exams are in person.

• The final exam is, by default, cumulative.

• Exam materials/questions may come from the suggested reading listed in the schedule.

• The midterm and final exams may be re-distributed into three exams. The midterm or final exam may be replaced by a project.

Announcements, communications, and discussions:

• We will make normal announcements using the Carmen Canvas.

• We will use Piazza for discussions. If you have questions about the course materials or policy, please post them on Piazza. The TA and I will also monitor these discussions and answer as appropriate, but students should be active and feel free to use the forums to have group discussions as well.

• Please only use email to contact the instructor or the TA for urgent or personal issues. Any e-mails sent to the instructor or TA should include the tag "[OSU-CSE-5523]" in the subject line. (This ensures we can filter and prioritize your messages.) We reserve the right to forward any questions (and their answers) to the entire class if they should prove relevant. Please indicate if you wish to be anonymized (i.e. have your name removed) in this case.

Policy (see the syllabus for details)

Homework:

• There are NO late days for homework assignments.

• Homework should be neat and professional and follow the required format. In particular, homework on torn sheets, scrap paper, or not well scanned into a single file will not be accepted.

• Homework is to be done individually. Of course, the discussion between students is allowed and encouraged, but the actual homework should be completed separately. You have to list with whom you discussed.

• Questions about homework or exams should be made in a timely fashion. Any complaints about homework grading must be made within 1-week of when the item is returned or before 12/14/2022 (whichever comes first). Do not wait until the end of the semester!

Exam:

• Excuse from scheduled exams can be accepted only in case of personal sickness requiring medical care or severe accidents in the immediate family (documentation required).

Schedule (tentative)