## Recent Advances in Deep Learning (EE807, Fall 2018)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. ## AnnouncementThe maximum number of course registrations is around 40, and we cannot accept more now (do not send an email to Instructor or TA). The deadline for notifying whether you choose A or B to TA is Sep. 2 (see Lecture 0). Two deadlines for sending your project reports to TA are Oct. 21 and Dec 16, which are all we need for your credit. Office hours: Every Tuesday, 11am - 1pm, N1-914 The final presentation for projects of Option A will be on Wednesday, Dec. 12 (10 minutes will be given for each student to talk). The claim for midterm project of Option B will be on Monday, Nov. 26, 1-3 PM at N1 917.
## ScheduleLecture 0: Introduction to EE807 Lecture 1: Introduction to neural networks Lecture 2: Stochastic gradient descent Lecture 3: Regularization Lecture 4: CNN architectures Lecture 5: Graphical models Lecture 6: RNN architectures Lecture 7: Deep reinforcement learning Lecture 8: Deep bayesian models Lecture 9: Generative adversarial networks Lecture 10: Auto-regressive/encoding and flow-based models Lecture 11: Transfer learning Lecture 12: Domain transfer and adaptation Lecture 13: Novelty detection for deep classifiers Lecture 14: Adversarial examples Lecture 15: Interpretable learning Lecture 16: Network compression Lecture 17: Meta learning Lecture 18: Advanced models for vision Lecture 19: Advanced models for language Lecture 20: Advanced models for speech
## Other Deep Learning LecturesIntroduction 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
## ContactInstructor: Jinwoo Shin, jinwoos@kaist.ac.kr |