AI Experience Laboratory
Fall 2022
Schedule: Tue/Thu 1:00pm-3:30pm
Location: GIST EECS C2, Haerim Hall (F1)
Instructor: Ue-Hwan, Kim (uehwan@gist.ac.kr)
Office: GIST Central Research Facilities (C11) 407
Office Hour: Tue 4pm-5pm or by appointment
TAs:
Jae-Won, Bae (jaewonbae@gm.gist.ac.kr)
Su-Ji, Jang (sujijang@gm.gist.ac.kr)
Notice
- [Quiz] Check your quiz scores here
- [Project] Check your project scores here
- [Schedule] No lab on Nov. 1st (Fall Festival)
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
- Deep Learning for Computer Vision @ Stanford Link
- Natural Language Processing with Deep Learning @ Stanford Link
- Programming for AI @ KAIST Link
- Deep Learning @ University of Amsterdam Link
- Deep Learning & Applied AI @ Sapienza University Link
- Learn PyTorch for Deep Learning @ ZTM Link
- Dive into Deep Learning (Aston Zhang et al., 2019) Link
Schedule
| Date | Topic | Quiz | Materials |
|---|---|---|---|
| 08-30 | [Lec 00] Introduction | Slides | |
| 09-01 | [Lec 01] Neural Networks; Pytorch Fundamentals | Recording Slides | |
| 09-06 | [Lab 01] Pytorch Fundamentals; Neural Networks | Questions Solution | |
| 09-08 | No Lecture (National Holidays) | ||
| 09-13 | [Lec 02] Convolutional Neural Networks (CNNs) | Quiz 01 | Slides |
| 09-15 | [Lab 02] Convolutional Neural Networks (CNNs) | Questions Solution | |
| 09-20 | [Lec 03] Word Embeddings | Quiz 02 | Recording Slides |
| 09-22 | [Lab 03] Word Embeddings | Questions Solution | |
| 09-27 | [Lec 04] Recurrent Neural Networks (RNNs) | Quiz 03 | Recording Slides |
| 09-29 | No Lecture (Students Sports Meeting) | ||
| 10-04 | [Lab 04] Recurrent Neural Networks (RNNs) | Questions Solution | |
| 10-06 | [Lec 05] Autoregressive Models | Quiz 04 | Recording Slides |
| 10-11 | [Lab 05] Autoregressive Models | Questions Solution | |
| 10-13 | [Lec 06] Transformers and Pretraining | Quiz 05 | Recording Slides |
| 10-18 | [Lab 06] Transformers and Pretraining | Questions Solution | |
| 10-20 | No Lecture (Midterm Preparation) | ||
| 10-25 | No Lecture (Midterm Period) | ||
| 10-27 | No Lecture (Midterm Period) | ||
| 11-01 | No Lecture (Fall Festival) | ||
| 11-03 | [Project] Team Meet-up & Proposal | Template Upload | |
| 11-08 | [Lec 07] Vision Transformers | Quiz 06 | Recording Slides |
| 11-10 | [Lab 07] Vision Transformers | Questions Solution | |
| 11-15 | [Lec 08] Graph Neural Networks (GNNs) | Quiz 07 | Recording Slides |
| 11-17 | No Lecture (GIST Foundation Day) | ||
| 11-22 | [Lab 08] Graph Neural Networks (GNNs) | Questions Solution | |
| 11-24 | [Lec 09] Meta Learning: Learning to Learn | Quiz 08 | Recording Slides |
| 11-29 | [Lab 09] Meta Learning: Learning to Learn | Questions Solution | |
| 12-01 | [Lec 10] Generative Adversarial Networks (GANs) | Quiz 09 | Slides |
| 12-06 | [Lab 10] Generative Adversarial Networks (GANs) | Questions Solution | |
| 12-08 | [Project] Project Presentation | Quiz 10 | Upload |
| 12-13 | No Lecture (Final Exam Period) | ||
| 12-15 | No Lecture (Final Exam Period) | ||