Spring 2024

Introduction to Deep Learning (딥러닝개론) - Graduate Course

Instructor: Dr. Muhammad Syafrudin

Course information

This course will introduce deep learning using Python and Keras, and is loaded with insights. You’ll learn practical techniques that can be applied to the real world, and important theory for perfecting neural networks. As you progress through each week, you’ll build your understanding through explanations and practical examples. You are expected to acquire the skills you need to start developing deep learning applications.

Lecture

Monday 화15:00-18:00 KST / Daeyang AI Center 센535

Office hours

Appointment by email (Office at AI Center 502호)

Prerequisites

Prior knowledge of Calculus, Linear Algebra, and Probability will be advantageous

Notes

Course material can be downloaded in ecampus and please be aware, 
that we will not publicly release the homework assignments this year.

Please note that:
-All lectures are in English.
-Weekly lecture topics may be adjusted or changed without prior notice depending on the understanding level during the class.

1. Presentation:
Your presentation will consist of a 25-30 minute talk followed by a 10-15 minute Q&A session.
** Since most of the course material is based on research papers, active class participation is crucial for this semester. Therefore, there are a couple of things you need to do before and during the class.

2. Once you have selected your paper, please inform the class by dropping a message in ecampus, stating "OOO presents OOO paper." Each student should choose their paper at least two weeks before their scheduled presentation.

3. For the paper you have selected, you should prepare your presentation. Additionally, you are required to write a homework assignment called "paper critique" for ALL the other papers selected by your classmates. This assignment involves creating a 1-2 page summary for each paper chosen by your peers. The paper critique should include the following content:
- 3 main points of the reading: What is the paper about? What problem does it address? What solution does it propose?
- 3 strengths of the reading: What is the main novelty of the paper? What is its impact? If you were to write such a paper, what aspects would you consider?
- 3 potential improvements for the reading: Are there any weaknesses in the paper? How can it be extended and improved?
- 3 questions you have about the paper.

4. You must submit a hard copy of the paper critique before the class. Additionally, you need to upload your presentation file and paper critique on ecampus prior to your presentation.

5. Each presentation will be followed by a 10-minute Q&A session. During the Q&A session, all students should actively engage by asking questions or discussing the paper. This is mandatory for all presentations.

6. If you have a suggestion for a paper that is related to our class, you can propose it with prior approval from me.

Schedule

주차(Week)	강의내용(Class Topic & Contents)	강의활동유형(Class Type)
1	Course overview (3/4)	Lecture and discussion
2	What is deep learning?, The mathematical building blocks of neural networks (3/11)	Lecture and discussion
3	Introduction to Keras and TensorFlow, Getting started with neural networks: Classification and regression (3/18)	Lecture and discussion
4	Fundamentals of machine learning, The universal workflow of machine learning (3/25)	Lecture and discussion
5	Working with Keras: A deep dive (4/1)	Lecture and discussion
6	Introduction to deep learning for computer vision (4/8)	Lecture and discussion
7	Deep learning for timeseries (4/15)	Lecture and discussion
8	Midterm exam (4/15)	시험 (Exam)
9	Deep learning for text (4/29)	Lecture and discussion
10	No class -- Public holiday (5/6)	No class
11	Paper with code presentation: ELSTOHY M. A. G. A., GAZA HAIFA (5/13)	Student presentation and discussion
12	Paper with code presentation: SRIWIJAYA Y. R., JORDAN D. J.(5/20)	Student presentation and discussion
13	Paper with code presentation: LE T. L., ABBAS REZK (5/27)	Student presentation and discussion
14	Paper with code presentation: ABDALLA AHMED, ABDULKHALEK ALFAKIH (6/3)	Student presentation and discussion
15	Paper with code presentation: AHMED S. A. E. M., SAID SALEM (6/10)	Student presentation and discussion
16	Paper with code presentation: 우수한 (6/17)	Student presentation and discussion

Grading

The final grade will be calculated using the following weights:

#	Final Grade Weight
Attendance	15%
Assignment(quiz/homework/weekly assignment) & presentations	45%
Mid exam	40%
Total	100%

Assignment

There might be one or two student presentations, and in addition, paper critiques for other students' presentations will be assigned and evaluated.

Submitting an assignment

Instructions for turning in assignments will be posted when the semester starts (in ecampus).

Getting help

For questions about homework, course content, installation, and after you have tried to troubleshoot yourselves, the process to get help is: Post the question in ecampus/group chat and hopefully your peers will answer. Note that in ecampus questions are visible to everyone. For private matters send an email to helpline: udin [at] sju [dot] ac [dot] kr.

Course Policies

Collaboration policy

We encourage you to talk and discuss the assignments with your fellow students (and on ecampus), but you are not allowed to look at any other students assignment or code outside of your pair. Discussion is encouraged, copying is not allowed.

Late day policy

Homework is due before each class. Late submission are not allowed.

Communication to students

Class announcements will be through ecampus. All homework and quizzes will be posted in ecampus. Also all feedback forms. Important note: make sure you have your settings set so you can receive emails from ecampus.

Academic honesty

We give a strong emphasis to Academic Honesty. As a student your best guidelines are to be reasonable and fair. We encourage teamwork for problem sets, but you should not split the homework and you should work on all the problems together.

Presented Papers

TBD.