AI Experience Laboratory
Fall 2025
Schedule: Mon/Wed 4:00pm-6:30pm
Location: GIST College Building C (N6), Room 110 (Zoom Online) / Class Colab
Instructor: Ue-Hwan, Kim (uehwan@gist.ac.kr)
Office: GIST Central Research Facilities (C11) 407
Office Hour: Tue 4pm-5pm or by appointment
TAs:
Won-Sic, Jang (wonsicjang@gm.gist.ac.kr)
Se-Hoon, Oh (ohsehoon@gm.gist.ac.kr)
Yi-Rum, Kim (kimyirum@gm.gist.ac.kr)
Notice
- Review report format available here
- Recitations start from September 10 :)
Introduction
This course will showcase various methods in machine learning and deep learning. Throughout the semester, emphasis will be put on practical use cases. Examples of specific methods this course covers includes convolutional neural networks, recurrent neural networks, transformers and generative adversarial networks. Further, we will use Google Colab as our development environment.
References
- Introduction to Deep Learning @ CMU Link
- Deep Learning @ Eberhard Karls Universität Tübingen Link
- Introduction to Deep Learning @ UW Link
- Deep Learning for Computer Vision @ Stanford Link
- Natural Language Processing with Deep Learning @ Stanford Link
- Learn PyTorch for Deep Learning @ ZTM Link
- Deep Learning from Scratch Link
- Dive into Deep Learning (Aston Zhang et al., 2019) Link
Schedule
| Date | Topic | Materials | Recitations |
|---|---|---|---|
| 09-01 | [Session 00.0] Introduction | Lecture Slides Submit Result | |
| 09-03 | [Session 01.0] Preliminary | Lecture Slides Submit Result | |
| 09-08 | [Session 01.1] Preliminary (cont'd) | Lecture Slides Submit Result | |
| 09-10 | [Session 02.0] Perceptrons | Lecture Slides Submit Result | Exercises solution |
| 09-15 | [Session 02.1] Perceptrons (cont'd) | Lecture Slides Submit Result | |
| 09-17 | [Session 03.0] Loss functions | Lecture Slides Submit Result | Exercises solution |
| 09-22 | [Session 03.1] Loss functions (cont'd) | Lecture Slides Submit Result | |
| 09-24 | [Session 04.0] Backpropagation | Lecture Slides Submit Result | Exercises solution |
| 09-29 | [Session 04.1] Backpropagation (cont'd) | Lecture Slides Submit Result | |
| 10-01 | [Session 04.2] Backpropagation (cont'd) | Lecture Slides Submit Result | Exercises solution |
| 10-06 | No Lecture (National Holiday) | ||
| 10-08 | No Lecture (National Holiday) | ||
| 10-13 | [Session 05.0] Optimization | Lecture Slides Submit Result | Exercises solution |
| 10-15 | [Session 05.1] Optimization (cont'd) | Lecture Slides Submit Result | |
| 10-20 | [Session 99.0] Midterm review (Q&A) | Exercises solution | |
| 10-22 | No Lecture (Midterm Preparation) | ||
| 10-27 | [Session 99.1] Midterm | ||
| 10-30 | No Lecture (Midterm Period) | ||