AI Experience Laboratory

Fall 2022

Schedule: Tue/Thu 1:00pm-3:30pm
Location: GIST EECS C2, Haerim Hall (F1)

Instructor: Ue-Hwan, Kim (
Office: GIST Central Research Facilities (C11) 407
Office Hour: Tue 4pm-5pm or by appointment

Jae-Won, Bae (
Su-Ji, Jang (


  • [Quiz] Check your quiz scores here
  • [Project] Check your project scores here
  • [Schedule] No lab on Nov. 1st (Fall Festival)


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.


  • Deep Learning for Computer Vision @ Stanford Link
  • Natural Language Processing with Deep Learning @ Stanford Link
  • Programming for AI @ KAIST Link
  • Introduction to Artificial Intelligence @ 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


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)