AIC3100:
Intro to Deep Learning and Applications
Course Information
Instructor: Seong Jae Hwang (seongjae@yonsei.ac.kr)
Instructor Office: 공학원 439b
Lecture Upload Date: Wednesday
TA:
Donghyun Kim / 김동현 (danny0103@yonsei.ac.kr)
Suhyun Kim / 김수현 (sshyunn@yonsei.ac.kr)
Changyoung Kim / 김찬영 (chanyoung@yonsei.ac.kr)
Gayoon Choi / 최가윤 (gynchoi17@yonsei.ac.kr)
Online TA Office Hours: Zoom links on LearnUs
We have two regular online office hours per week. Zoom links will be shared in LearnUs.
Tuesday 4:00PM~5:00PM
Thursday 3:00PM~4:00PM
Course Description
This course introduces the fundamentals of deep learning and discusses relevant applications. This introductory course is for those with little to no background in artificial intelligence or deep learning. Thus, the course covers the very basics of varying topics including linear algebra, artificial intelligence, machine learning, computer vision, and deep learning, mostly at a high level to introduce the audience to this field. Some recent trends in deep learning will be covered in the latter part of the course.
Pre-requisites
Linear algebra is useful but not necessary
Programming languages (tentative)
Python. Coding assignments in Jupyter Notebook file (.ipynb) in an open-source IDE Jupyter Notebook or Jupyter Lab. Other IDEs are also allowed as long as you can use .ipynb.
Useful but not necessary textbooks
Deep Learning: Ian Goodfellow, Yoshua Bengio, Aaron Courvile
Each week on learnus, you will see:
Slides
Lectures
Exams
Midterm and Final Exams
NOT in person
Details to be updated
Discussion Board:
Classum for public, anonymous Q&A.
Search for similar questions. You may find the answers you need in other Q&A.
Freely ask questions anonymously and publicly to help you and others.
Freely answer questions anonymously (which will be endorsed by the instructor).
Instructor and TAs will try their best to respond within 48 hours.
Course Policies
Grading for AIC3100.01-00 (tentative)
Attendance (5%)
Quizzes / Hands-on Assignments (15%)
Midterm (40%)
Final (40%)
Grading for AIC3100.GD-00 (tentative)
Attendance (30%)
Quizzes / Hands-on Assignments (10%)
Midterm (30%)
Final (30%)
Attendance
Online-attendance (i.e., play the lecture videos)
You have 1 week to watch the lecture video (must complete >90%)
-0.5 for each absence
Collaboration Policy and Academic Honesty
You will do your work (exams and homework) individually.
The work you turn in must be your own work.
You are allowed to discuss the assignments with your classmates, but do not look at the code they might have written for the assignments, or at their written answers.
You are not allowed to search for code on the internet, use solutions posted online unless you are explicitly allowed to look at those, or to use Python's implementation if you are asked to write your own code.
When in doubt about what you can or cannot use, ask the instructor!
Posting and asking HW questions online (e.g., Stack Overflow) is NOT allowed. This will be considered as cheating.
A first offense will cause you to get 0% credit on the assignment. A report will be filed with the school.
A second offense will cause you to fail the class and receive a disciplinary penalty.
Note on Disabilities
If you have a disability for which you are or may be requesting accommodation, you are encouraged to contact your instructor ASAP.
