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Class time: Wednesday and Friday, 2:20 PM - 3:40 PM
Classroom: Scott Lab E024
Course website: https://sites.google.com/view/osu-cse-3521-sp24-chao
Instructor: Prof. Wei-Lun (Harry) Chao https://sites.google.com/view/wei-lun-harry-chao
Email: chao.209@osu.edu
Office hours: Monday 5:30 pm - 6:30 pm and Thursday 5:30 pm - 6:30 pm, at DL587
TA: Ping Zhang
Email: zhang.14217@osu.edu
Office hours: Tuesday 9:15 am - 10:15 am and Friday 10:15 am - 11:15 am, at Baker System 406, Station 2
Course information
Course information
Course abstract: Survey of basic concepts and techniques in artificial intelligence, including problem-solving, knowledge representation, and machine learning.
Course objectives:
Be familiar with basic search techniques for problem-solving.
Be exposed to multiple knowledge-representation formalisms.
Be familiar with data and feature representations.
Mastery of basic unsupervised learning techniques and the kinds of problems they solve.
Mastery of basic supervised learning techniques and the kinds of problems they solve.
Be exposed to the ethics of AI.
Course credits: 3 units
Pre-requisites:
Required background:
§ CSE 2331 (Foundations 2) or 5331
§ Linear algebra: Math 2568, 2174, 4568, or 5520H
§ Statistics and probability: Stat 3201 or 3450 or 3460 or 3470 or 4201 or Math 4530 or 5530H
Students in the class are expected to have a reasonable degree of mathematical sophistication and to be familiar with the basic knowledge of linear algebra, multivariate calculus, probability, and statistics. Students are also expected to have knowledge of basic algorithm design techniques and basic data structures.
Programming in Python 3 is required.
Review materials can be found: linear algebra, probability, Python-1, Python-2, Python-3
Syllabus: Click
Required Textbook: No required textbook
Suggested references:
Stuart Russell and Peter Norvig, Artificial intelligence: a modern approach (3rd edition). Pearson, 2010
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. MIT Press, 2016.
Ethem Alpaydin, Introduction to Machine Learning. The MIT Press.
Useful references:
Kaare Brandt Petersen and Michael Syskind Pedersen, The Matrix Cookbook
Grading (tentative):
Homework: 40%
Quizzes & participation: 10%
Midterm exams: 15% + 15% (30%)
Final exam (Apr 24, 4:00 pm - 5:45 pm): 20%
There will be two midterm exams.
Homework: There will be 4-5 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 or Google Colab) will be used for submission. You must strictly follow the homework and submission instructions. For the problem set and report of the programming set, we may only allow PDF online submission.
Participation & quizzes: We will use Carmen quizzes.
Midterm exam & final exam: The midterm and final exams will be in person.
The final exam is, by default, cumulative.
There will be two midterm exams.
Announcements, communications, and discussions:
We will make announcements using the Carmen website or Piazza. 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 or Carmen for discussions. If you have questions about the course materials or policy, please also post them on these platforms. The TA and I will also monitor these discussions and answer as appropriate, but students should 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-3521]" 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)
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 4/24/2024 (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).
(Tentative) Weekly schedule
(Tentative) Weekly schedule
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