AI Experience Laboratory

Fall 2023

Schedule: Mon/Wed 4:00pm-6:30pm
Location: GIST College Building A (N4), Room 227 (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:
Ji-Ae, Yoon (jiaeyoon@gm.gist.ac.kr)
Jae-Woo, Kim (kjw01124@gm.gist.ac.kr)

Notice

  • Review report format available here
  • Recitations start from September 6 :)
  • Please give your class feedback through this link until September 27!

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
  • 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

Schedule

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Date Topic Materials Recitations
08-28 [Session 00.0]  Introduction Lecture Slides Submit Result
08-30 [Session 01.0]  Preliminary Lecture Slides Submit Result
09-04 [Session 01.1]  Preliminary (cont'd) Lecture Slides Submit Result
09-06 [Session 02.0]  Perceptrons Lecture Slides Submit Result Exercises Solution
09-11 [Session 02.1]  Perceptrons (cont'd) Lecture Slides Submit Result
09-13 [Session 03.0]  Loss functions Lecture Slides Submit Result Exercises Solution
09-18 [Session 03.1]  Loss functions (cont'd) Lecture Slides Submit Result
09-20 [Session 04.0]  Backpropagation Lecture Slides Submit Result Exercises
09-25 [Session 04.1]  Backpropagation (cont'd) Lecture Slides Submit Result
09-27 No Lecture (National Holiday)
10-02 No Lecture (National Holiday)
10-04 [Session 04.2]  Backpropagation (cont'd) Lecture Slides Submit Result
10-09 No Lecture (National Holiday)
10-11 [Session 05.0]  Optimization
10-16 [Session 05.1]  Optimization (cont'd)
10-18 [Session 99.0]  Midterm Review
10-23 [Session 99.1]  Midterm
10-25 No Lecture (Midterm Period)