Machine Learning and Deep Learning
An introductory course on traditional statistical machine learning. Topics include linear regression, classification, sampling methods, model selection, tree-based methods, support vector machine, survival analysis, hypothesis testing, unsupervised learning, and deep learning. Prior knowledge in basic statistics, linear algebra, and programming (Python) is recommended.
- Time: Mon/Wed 1:00pm-2:30pm (Fall 2025)
- Venue: GIST College Building C (N6), Room 104
- Codes: AI5213 / EC4213 / AI4021 / CT5303 / ET5402 / FE5402
- Size: 116 (75 undergraduates, 41 graduates)
Notice
- HW1 is released (Due: 9/21)
- The semester will begin at 9/1. Enjoy your vacation!
Useful Links
- Access the AI5213/EC4213 Ed Discussion forum.
- If you haven’t already been added to the Ed discussion, use this invitation link.
- Use your full name in the format “Firstname Lastname” with the first letter of each name capitalized (e.g., Sundong Kim).
- Don’t forget to use your valid email address (gm.gist.ac.kr).
- Submit your assignments at the AI5213/EC4213 Gradescope (Entry Code: PGEDDD). Make sure to enter your correct student ID.
- Use the same name format as for Ed Discussion: “Firstname Lastname” with the first letter of each name capitalized.
- Make sure to enter your correct student ID.
Textbooks
Syllabus
This course will utilize presentation slides by the textbook authors. Download all slides.
(Tentative schedule)
Staffs
Communication: The course schedule and all resources (e.g. lecture slides) will be posted on this course website. All class discussions, announcements and communication will take place via Ed Discussion. Students are strongly encouraged to post their questions as public posts so that everyone in the class can benefit from the discussion. Only in cases of truly personal matters should private posts be used. Course-related emails will not be checked, so please do not send emails to the instructors or TAs. All questions and communications should be made through Ed, and we aim to respond within two business days, often sooner. In addition, you are welcome to visit the TAs during office hours.
Sundong Kim
Instructor
QnA: Available after class (Lecture room)
Office: AI Building (S7), Room 204
Jiwon Park
TA
Mon 4:00-5:30pm
Office: AI Building (S7), Room 208
Woochang Sim
TA
Tue 4:00-5:30pm
Office: AI Building (S7), Room 208
Hyunseok Ryu
TA
Wed 4:00-5:30pm
Office: AI Building (S7), Room 208
Heejun Kim
TA
Tue 4:00-5:30pm
Office: AI Building (S7), Room 208
Seojin Hwang
TA
Thu 4:00-5:30pm
Office: AI Building (S7), Room 206
Jimin Jeon
TA
Fri 4:00-5:30pm
Office: TBA
Homeworks
Submit your assignments at the AI5213/EC4213 Gradescope. Homework will be released at least two weeks before the deadline.
- HW 1 is due Sunday, Sep 21 at 11:59PM [PDF] [TeX]
- HW 2 is due Sunday, Oct 19 at 11:59PM
- HW 3 is due Sunday, Nov 16 at 11:59PM
- HW 4 is due Sunday, Dec 7 at 11:59PM
Late policy for homeworks: If assignments are late, they are increasingly penalized as follows: within 24 hours, you lose 10%; within 48 hours, you lose 20%; within 72 hours, you lose 40%. More than three days late, you can no longer hand-in the assignment.
Exams
There will be two closed-book exams. You are allowed to bring one handwritten A4 crib sheet (both sides), printed sheets or those created by iPad are not allowed. Submit your crib sheet with your exam sheet for bonus points. For the exams, you may only bring: your student ID, pen, pencil, eraser, and your handwritten crib sheet. No other materials are allowed.(e.g., mobile phone, iPad, laptop, books).
- The midterm will take place on Monday, Oct 27, 1:00pm (Venue: TBD, 100 minutes)
- The final exam will take place on Monday, Dec 15, 1:00pm (Venue: TBD, 120 minutes)
Exams will cover material from lecture, homework, book, and equivalent.
- For midterm, ISLP Ch.1-9 and relevant materials will be covered.
- For finals, ISLP Ch.10-13, Ch.1-9 and relevant materials will be covered.
Exam Conflicts and Makeups: Exam makeups will only be scheduled for approved conflicts. If you have a conflict with any of the exams, let us know as soon as possible. Examples include:
- Acceptable reasons: Presenting at a major academic conference, family emergencies (e.g., illness or passing of a close relative), or other serious unforeseen circumstances.
- Unacceptable reasons: Having a heavy exam schedule on the same day (e.g., another exams at 9am–12pm and 4pm–7pm).
Gradings
You will earn A if (but not only if) your score is at least \(80\times(1-ε_1)\), B if your score is at least \(60\times(1-ε_2)\), C if your score is at least \(40\times(1-ε_3)\), for some \(ε_i ≥ 0\) to be determined later. All participants in the course (Undergraduate and graduate students) are evaluated equally, regardless of the course codes.
(Tentative breakdown)
- Four homework (40%)
- Midterm (25%)
- Final Exam (35%)
- Some bonus points for active participants
Collaboration Policy & Honor Code
Study groups are allowed. It is also OK to get clarification (but not solutions) from books or online resources, again after you have thought about the problems on your own. However, we expect students to understand and complete their own assignments. Each student must write down the solution independently and hand in one assignment per student, which means you write your solution after closing the book and all your notes, without helped by your colleagues. If you studied together as a group, please cite your collaborators fully and completely at the top of your assignment (e.g., “Junho explained to me what is asked in Question 2.1”). When in doubt about collaboration details, please ask us on Ed discussion.
If elements of two assignments are determined to be clearly very similar, we believe that they were done together or one was copied from the other, or they all copied from AI assistant), then the course grade for all students involved in the incident will be reduced by one letter grade for the first offense, and to an F for the second offense. (All means both the copy-ers and the copy-ees). The grade for that assignment will also be reduced to 0. More serious cases of cheating (e.g., cheating on exams) will lead to severe consequences ranging from a grade of “F” on the class to suspension from the University.
Message from the Instructor
I am happy to lead this course, but having a large number of students can be challenging. Additionally, as this course is relatively new, the course management may be subject to adjustments, and the workload might be unexpected for some of you. The course deals with the basics of traditional machine learning, which might be different from what you’re hoping to study.
To get a better grasp of what you’ll learn in this class, I strongly encourage you to browse through the textbooks, especially the ISLP book. It will give you a good overview of the course content and help you decide if this class aligns with your learning goals.
I would suggest taking the following courses this semester if you are interested in:
- Hands-on practice on deep learning: Take AI5214 (AI Experience Lab)
- Deep learning: Take AI5302 (Deep Learning)
- Advanced deep learning: Take RT5102 (Advanced Deep Learning)
- Reinforcement learning: Take AI5305 (Reinforcement learning)
- Advanced machine learning: Take EC6304 (Pattern Recognition)
- Real-world AI project: Take AI4028 (AI Core Technology Based Project)
- LLM, Cloud, Gen AI: Take AX5301 (Principles and Emerging Technology in AI)
- Fairness and policy: Take AI5750 (인공지능 정책, Korean)
Please consider your interests and goals carefully when deciding whether to take this course or one of the alternatives listed above.
