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Fall 2023

Topics in Machine Learning (기계학습특론) - Graduate Course

Instructor: Dr. Muhammad Syafrudin

Course information

The objectives of this course are to explore different topics in machine learning, such as Regression, Deep Learning, Convolutional Neural Networks and Self-Attention, Transformers, Generative Models, Explainable AI, Lifelong Learning, and Meta Learning. Through this class, students will gain new insights into recent developments in machine learning, allowing them to deepen their knowledge in areas of interest. Additionally, the course aims to foster research exchange and facilitate discussions among students.


Tue at 15:00‐18:00 am KST at 광925

Office hours

Appointment by email (Office at AI Center 502호)


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


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.


주차(Week)강의내용(Class Topic & Contents)강의활동유형(Class Type)
1Course introduction (9/5)Lecture and discussion
2Intro to ML - regression (9/12)Lecture and discussion
3Deep Learning (9/19)Lecture and discussion
4Convolutional Neural Networks & Self-Attention (9/26)Lecture and discussion
5No class -- Public holiday: National Foundation Day (10/3)No class
6Transformer (10/10)Lecture and discussion
7Paper presentation - round one: 4 students (10/17)Student presentation and discussion
8Midterm exam (10/24)시험 (Exam)
9Paper presentation - round one: 4 students (10/31)Student presentation and discussion
10Generative Model (11/7)Lecture and discussion
11Explainable AI (11/14)Lecture and discussion
12Lifelong Learning (11/21)Lecture and discussion
13Meta Learning (11/28)Lecture and discussion
14Paper presentation - round two: 4 students (12/5)Student presentation and discussion
15Paper presentation - round two: 4 students (12/12)Student presentation and discussion
16Final exam (12/19)시험 (Exam)


The final grade will be calculated using the following weights:

#Final Grade Weight
Assignment(quiz/homework/weekly assignment)30%
Mid exam30%
Final exam30%


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

  • Khan, A. I., Shah, J. L., & Bhat, M. M. (2020). CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine, 196, 105581.
  • Çolak, A. B., Shafiq, A., & Sindhu, T. N. (2022). Modeling of Darcy–Forchheimer bioconvective Powell Eyring nanofluid with artificial neural network. Chinese Journal of Physics, 77, 2435–2453.
  • Xiang, C., Wang, D., Pan, Y., Chen, A., Zhou, X., & Zhang, Y. (2022). Accelerated topology optimization design of 3D structures based on deep learning. Structural and Multidisciplinary Optimization, 65(3), 99.
  • Sours, T. G., & Kulkarni, A. R. (2023). Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials. The Journal of Physical Chemistry C, 127(3), 1455–1463.
  • De Leon, C. L. C.-D., Vergara-Limon, S., Vargas-Trevino, M. A. D., Lopez-Gomez, J., Gonzalez-Calleros, J. M., Gonzalez-Arriaga, D. M., & Vargas-Trevino, M. (2022). Parameter Identification of a Robot Arm Manipulator Based on a Convolutional Neural Network. IEEE Access, 10, 55002–55019.
  • Pang, S., Morris, D., & Radha, H. (2022). Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 3747–3756.
  • Wu, J., Cao, M., Cheung, J. C. K., & Hamilton, W. L. (2020). TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 5730–5746.
  • Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents.
  • Vödisch, N., Cattaneo, D., Burgard, W., & Valada, A. (2023). CoVIO: Online Continual Learning for Visual-Inertial Odometry. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2464–2473.
  • Chen, H., & Bajorath, J. (2023). Meta-learning for transformer-based prediction of potent compounds. Scientific Reports, 13(1), 16145.
  • Wang, P., Wu, Q., Shen, C., Dick, A., & Van Den Hengel, A. (2018). FVQA: Fact-Based Visual Question Answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10), 2413–2427.
  • Reddy, P. B. A., & Das, R. (2016). Estimation of MHD boundary layer slip flow over a permeable stretching cylinder in the presence of chemical reaction through numerical and artificial neural network modeling. Engineering Science and Technology, an International Journal, 19(3), 1108–1116.
  • Meng, X., & Karniadakis, G. E. (2020). A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems. Journal of Computational Physics, 401, 109020.