From: Dawn Song <dawns...@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 -- Liberationtech is public & archives are searchable on Google. Violations of list guidelines will get you moderated: https://mailman.stanford.edu/mailman/listinfo/liberationtech. Unsubscribe, change to digest, or change password by emailing moderator at compa...@stanford.edu.
