Deep learning has revolutionized many subfields within AI. DeepMind's AlphaGo combined convolutional neural networks together with deep reinforcement learning and MCTS, and won many games against top human Go players. In computer vision, most of the leading systems in ImageNet competitions are based on deep neural networks. Deep learning has also changed the game in NLP: for example, Google has recently replaced their phrase-based machine translation system with neural machine translation system. Throughout the quarter, we will go over some of the basics in neural networks, and we will also go through the deep learning revolution after 2006. In this graduate class, we will also emphasize on the development of graduate student's paper reading and presentation abilities: each student will need to present research papers related to this topic. Last but not least, the most important aspect of this course is for students to work on a novel research project in open problems related to NLP and deep learning, and gain hands-on experiences of doing cutting-edge research.
Prerequisites:
CS 165B Machine Learning with a B or better
Good programming skills and knowledge of data structure (e.g., CS 130A)
Solid background in machine learning, linear algebra, probability, and calculus.
Solid background in machine learning, linear algebra, probability, and calculus.
Comfortable with deep learning platforms such as TensorFlow, Torch, Theano, MXNet, Caffe etc.
Prior experiences with AWS is not required, but could be very useful.
Course Objective
At the end of the quarter, students should have a good understanding about basic deep learning models, and should be able to implement some fundamental algorithms for simple problems in deep learning. Students will also develop an understanding of the open research problems in deep learning, and be able to conduct cutting-edge research with novel contributions to improve existing solutions.