Recent Advances in Deep Learning (AI602, Fall 2019)

Deep learning is a new area of machine learning research, which have demonstrated states-of-the-art performance on many artificial intelligence tasks, e.g., computer vision, speech recognition and natural language processing. In this lecture, I will cover some of recent advances (made mostly in the last 5 years) in this area.

Announcement

• The maximum number of course registrations is around 40, and we cannot accept more now (do not send an email to Instructor or TA).

• Office hours: Every Tuesday, 11am - 1pm, N1-914

Schedule

• Lecture 0: Introduction to AI602

• Lecture 1: Introduction to neural networks

• Lecture 2: Optimization and regularization

• Lecture 3: CNN architectures

• Lecture 4: RNN architectures

• Lecture 5: Transfer and multi-task learning

• Lecture 6: Adversarial examples

• Lecture 7: GNN architectures

• Lecture 8: Deep generative models I: GAN

• Lecture 9: Deep generative models II: VAE and others

• Lecture 10: Novelty and uncertainty estimation

• Lecture 11: Domain transfer and adaptation

• Lecture 12: Deep reinforcement learning

• Lecture 13: Unsupervised and semi-supervised learning

• Lecture 14: Network compression

• Lecture 15: Meta learning

• Lecture 16: Interpretable learning

• Lecture 17: Object detection and segmentation

• Lecture 18: Visual question answering

Other Deep Learning Lectures

• Introduction to Deep Learning (6.S191), Massachusetts Institute of Technology

• Introduction to Deep Learning (11-785), Carnegie Mellon University

• Deep Learning (CS1470), Brown University

• Theories of Deep Learning (STATS385), Stanford University

• Theoretical principles for deep learning (IFT 6085), University of Montreal

• Deep Learning (EE559), École Polytechnique Fédérale de Lausanne

• Deep Reinforcement Learning (CS294-112), UC Berkeley

• Natural Language Processing with Deep Learning (CS224n), Stanford University

• Deep Learning for Self-Driving Cars (6.S094), Massachusetts Institute of Technology

Contact

Instructor: Jinwoo Shin (jinwoos@kaist.ac.kr)
TAs: Younggyo Seo (younggyo.seo@kaist.ac.kr) and Seunghyun Lee (camusian@kaist.ac.kr)