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[liberationtech] New UC Berkeley Course Announcement: CS294: Special Topics in Deep Learning

Yosem Companys companys at stanford.edu
Mon Aug 22 12:10:00 PDT 2016


From: Dawn Song <dawnsong at cs.berkeley.edu>

CS294: Special Topics in Deep Learning

Instructor and co-instructors: Trevor Darrell, Sergey Levine, and Dawn Song

Time: Wed 10am-noon (First class starts on Aug 31)

Location: To be announced

Course website: https://people.eecs.berkeley.edu/~dawnsong/cs294-dl.html

Course mailing list: join at
https://groups.google.com/forum/#!forum/cs294-dl-f16 for future
updates.

Course description:

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.

  Looking forward to you joining us!


thanks,
Trevor, Sergey, and Dawn



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