Class time: Tuesday and Thursday, 12:45 pm - 2:05 pm
Classroom: Pomerene Hall 150
Course website: https://sites.google.com/view/osu-cse-5523-sp23-chao
Instructor: Prof. Wei-Lun (Harry) Chao https://sites.google.com/view/wei-lun-harry-chao
Email: chao.209@osu.edu
Office hours: Tuesday 2:15 - 3:00 pm, Thursday 10:00 - 11:00 am (DL 587)
TA: Ziwei Li
Email: li.5326@osu.edu
Office hours: Monday and Friday 12:00 - 1:00 pm (BE406 station#5)
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
Also, check HERE for a set of slide decks for linear algebra
Required Textbook: No required textbook
Suggested references:
Christopher M Bishop, Pattern recognition and machine learning. Springer, 2006.
Kevin P. Murphy, Machine Learning: A Probabilistic Perspective. The MIT Press, 2012
Other suggested references:
Shai Shalev-Shwartz and Shai Ben-David, Understanding machine learning: From theory to algorithms. Cambridge university press, 2014.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep learning. The MIT Press, 2016.
Sergios Theodoridis, Machine Learning: A Bayesian and Optimization Perspective. Academic Press, 2016/2020.
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, Foundations of Machine Learning, The MIT Press, 2012/2018
Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin, Learning from Data, AMLBook, 2012.
Ethem Alpaydin, Introduction to Machine Learning. The MIT Press.
Useful reference:
Kaare Brandt Petersen and Michael Syskind Pedersen, The Matrix Cookbook
Grading (tentative):
Homework: 60%
Midterm exam: 20%
Final exam (4/27, 14:00 - 15:45 ET): 20%
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 6 homework assignments.
Each assignment will include a problem set and a programming set.
Programming in Python 3 is required.
Carmen (and other platforms like GitHub) will be used for submission.
For the problem set and the report of the programming set, we may only allow pdf submission with your answers typed in LaTeX (for example, you may use overleaf).
You must strictly follow the homework and submission instructions.
Quizzes:
We will use Carmen quizzes if there are any.
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. Announcements of urgent matters will be mailed to your name.#@osu.edu address. If you do not regularly read that account, make sure you forward it to somewhere that does.
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.
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 5/1/2023 (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).