AI Experience Laboratory

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:
Jae-Woo, Kim (kjw01124@gm.gist.ac.kr)
Won-Sic, Jang (wonsicjang@gm.gist.ac.kr)

Notice

• Review report format available here
• Recitations start from September 11 :)
• We do not have recitation on October 9 (we will cover the material on October 16).
• [Midterm] Date: Oct. 28, 4pm-6pm / Location: GIST Oryong Hall (W1) Room 203
  • The exam is closed book, closed notes, closed computer, and closed calculator
  • Just need to bring your pen, pencil and erasers in addition to your student ID card
  • Coverage: Session 00 ~ Session 05 (including recitations)
  • Result released! Check this document
    • Claim during Dec. 3, 7pm-8pm C11 Room 410
• We do not have recitation on November 6
• RR Result (~midterm)
  • Check the result here
  • The meaning of scores
    • 0.50: not sufficient amount of content
    • 0.25: link is broken
  • RR claim will be on Nov. 20 after the recitation
    • You can claim for the broken link showing the edit history
• [Final Exam] Date: Dec. 16, 4pm-6pm / Location: GIST College Building C (N6) Room 104
  • The exam is closed book, closed notes, closed computer, and closed calculator
  • Just need to bring your pen, pencil and erasers in addition to your student ID card
  • Coverage: Session 06 ~ Session 10 (including recitations)

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