Class time: Tuesday and Thursday, 14:20 - 15:40

Classroom: Carmen Zoom

Course website: https://sites.google.com/view/osu-cse-3521-5521

Instructor: Prof. Wei-Lun (Harry) Chao

Email: chao.209@osu.edu

Office hour: Tuesday 11:00-12:00 & Thur 18:00-19:00, using Carmen Zoom

TA: Prashant Serai

Email: serai.1@osu.edu

Office hour: Monday 17:00-18:00 & Thursday 10:00-11:00, using Carmen Zoom

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 formalism.

• Be familiar with data and feature representations.

• Mastery of basic unsupervised learning techniques and the kinds of problem they solve.

• Mastery of basic supervised learning techniques and the kinds of problem they solve.

• Be exposed to the ethics of AI

Course credits: 3 units

Pre-requisites:

• Required: CSE 2331 (Foundations 2)

• Suggested: MATH 3345 (Foundations of Higher Math), Stat 3460 or 3470 (Intro to Probability and Stats), MATH 2568 (Linear Alg)

• 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 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:

• Christopher M Bishop, Pattern recognition and machine learning. Springer, 2006.

• Stuart Russell and Peter Norvig, Artificial intelligence: a modern approach (3th edition). Pearson, 2010

Other suggested reference:

• 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.

Useful reference:

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

Homework: There will be 4 homework assignments. Each assignment will include a problem set and a programming set. Programming in Python is required. Carmen (and other platforms like GitHub or Google Colab) will be used for submission. For the problem set and report of the programming set, we may only allow pdf submission with your answers typed in LaTeX.

Participation & quizzes: We will use Carmen Zoom to record participation. We will use Carmen quizzes.

Midterm exam & final exam: We will use Proctorio or Carmen quizzes or Carmen Zoom.

About on-line lectures using Carmen Zoom:

• Please use your full name (e.g., Wei-Lun Chao).

• Please update your photo in Carmen Zoom.

• Upon joining the meeting, please mute yourself.

• You are encouraged to ask questions during the lecture by raising your hand (in participation) or typing questions (in chats). I will then ask you to unmute yourself to speak.

• We will record the lectures. The videos will be shared with you after the lectures.

• We recommend that you use cameras, especially during the office hours.

Announcements, communications, and discussions:

• We will make normal announcements using 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 for discussions. If you have questions about the course materials or policy, please also post them on Piazza. 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 "[3521]" or "[5521]" 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, 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 12/9/2020 (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).