This course provides an introductory overview of modern techniques for learning from data. Students will learn the theories and applications of machine learning and deep learning. Topics include various neural network architectures – feedforward neural networks, convolutional neural networks, recurrent neural networks, transformers, large language models, graph neural networks, autoencoders, and deep generative models – along with optimization and regularization techniques for neural networks.
* This course introduces advances in deep learning, assuming that you have a basic understanding of machine learning and the required fundamental mathematics (Linear Algebra, Probability and Statistics). I will briefly review some prerequisite concepts, but not comprehensively.
** This course does not cover implementation using deep learning frameworks such as PyTorch or TensorFlow. But, you will need to use these frameworks using your own computational resources for your term project.
*** If you do not meet these prerequisites, I strongly recommend that you drop the course.
Class Time: Friday 12:00~14:45
Location: 26421 (Engineering Building 2)
Language: Korean
Prof. Seokho Kang
Office: 27408B (Engineering Building 2)
E-mail: s.kang@skku.edu
Office Hours: by appointment
Mr. Woojae Kim
Office: 27407 - Data Mining Lab. (Engineering Building 2)
E-mail: keemwoojae at gmail.com
Ian Goodfellow, Yoshua Bengio & Aaron Courville, Deep Learning, MIT Press, 2016. (https://www.deeplearningbook.org/ )
Aston Zhang, Zachary C. Lipton, Mu Li, Dive into Deep Learning, Cambridge University Press, 2023. (https://d2l.ai/)
Trevor Hastie, Robert Tibshirani & Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.), Springer, 2009. (https://web.stanford.edu/~hastie/ElemStatLearn/)
PyTorch Tutorials (https://pytorch.org/tutorials)
Attendance (10%)
Assignments (5%)
Presentation (10%)
Term Project (15%)
Mid-term Exam (30%)
Final Exam (30%)
Total (100% + a)
Syllabus [download]
Course Introduction [download]
1. Machine Learning Basics [download]
2. Deep Neural Networks [download]
3. Optimization [download]
4. Regularization [download]
5. Practical Methodology [download]
6. Convolutional Neural Networks [download]
7. Recurrent Neural Networks [download]
8. Transformers [download]
9. Large Language Models [download]
10. Graph Neural Networks [download]
11. Autoencoders [download]
12. Deep Generative Models [download]
13. Further Topics in Deep Learning [download]
Invited Lecture: Agentic AI시대의 Personalization AI (LG U+ Personal Agent기술팀 전영환 팀장)
All assignments should be submitted to icampus by midnight of the due date. Late submissions will NOT be accepted.
[A1] Self-Introduction
[A2] Term Project Presentation
Students are required to give a 10-minute in-class presentation. The goal is to share recent advances in deep learning by presenting journal papers or conference proceedings (not necessarily their own), published within the last five years and related to topics covered in the course. The presentation topic should be relevant to the lecture topic of the same week. Students must schedule their presentation in Week 6 using the form provided by the instructor. Presentation materials must be submitted to icampus at least one day before the presentation.
For the term project, students are required to participate in one of the selected competitions. The list of eligible competitions will be provided by the instructor in Week 6. Students may work individually or in teams of up to three members. Evaluation will be based on the team’s public ranking on the competition leaderboard, as well as the overall completeness and quality of the final report. The report must clearly describe the problem being addressed, data preprocessing steps, model architecture, learning objectives, training strategies, and evaluation results. It should also include reflections on what worked well, challenges encountered, and suggestions for future improvements. The project must use at least one of the following model backbones: CNN, RNN, GNN, Transformer, or Vision Transformer.
Students are responsible for maintaining high standards of academic integrity in all of their class activities. Cheating or plagiarism in any form will not be tolerated. Any violation of academic integrity is a serious offense and is therefore subject to an appropriate sanction or penalty.