Trevor Darrell, Sergey Levine, Dawn Song

Chang Liu, Richard Shin

Wed 10:00am-noon (First class starts on Aug 31)
location TBD

cs294-dl-f16@googlegroups.com
Please sign up the course mailing list at https://groups.google.com/forum/#!forum/cs294-dl-f16 for future updates.

If you do not plan to take the class, but are interested in getting announcements about guest speakers in class, and more generally, deep learning talks at Berkeley, please sign up the mailing list https://groups.google.com/forum/#!forum/berkeley-deep-learning for future announcements.

Date	Topic	Reading Assignments & Guest Lectures
8/31/2016	Introduction	Guest speaker: Kaiming He Reading: Deep Learning, by Yann LeCun, Yoshua Bengio, and Geoffrey Hinton.

In recent years, deep learning has enabled huge progress in many domains including computer vision, speech, NLP, and robotics. It has become the leading solution for many tasks, from winning the ImageNet competition to winning at Go against a world champion. This class is designed to help students develop a deeper understanding of deep learning and explore new research directions and applications of deep learning. It assumes that students already have a basic understanding of deep learning. In particular, we will explore a selected list of new, cutting-edge topics in deep learning:

• Security and privacy issues in deep learning. First, we will explore attack methods and defenses in the area of adversarial deep learning, where attackers can purposefully generate adversarial examples to fool state-of-the-art deep learning systems. Second, we will explore the area of privacy-preserving deep learning. A deep learning system trained over private data could memorize and leak private information undesirably. We will explore areas including model-inversion attacks and how to provide differential privacy guarantees for deep learning algorithms. Finally, we will explore the use of deep learning in security applications such as malware and fraud detection.
  

• Novel application domains of deep learning, beyond the mainstays of computer vision and speech recognition. First, we will explore new techniques in deep reinforcement learning, involving both applications of reinforcement learning to traditionally supervised learning problems and applications of deep learning to tasks that involve decision making and control. Second, we will explore new domains at the intersection of deep learning and program synthesis and formal verification. We will also explore other new application domains such as using deep learning for graph analysis.
  

• Recent advances in the theoretical and systems aspects of deep learning. First, we will cover the recent advances in generative models, including variational autoencoders and generative adversarial networks. Second, we will explore new theoretical advances in understanding deep learning such as the Deep Rendering Model. Third, we will explore new system and architectural advances in scaling up deep learning including TensorFlow, MxNet and new architectural designs.
  

This is a lecture, discussion, and project oriented class. Each lecture will focus on one of the topics, including a survey of the state-of-the-art in the area and an in-depth discussion of the topic. Each week, students are expected to complete reading assignments before class and participate actively in class discussion.

Deadlines:
Questions submission is due by Sunday on 7pm
Voting on the submitted questions is due by Mon midnight

Students will also form project groups and complete a research-quality class project. Groups will consist of one to three students.

Course #: COMPSCI 294-131 (Class #34939)

Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville