Instructor: Professors from IIIS, Tsinghua University

Research Practice is a practical course in which students conduct research practices for one semester in renowned institutes both at home and abroad. Each student will be assigned a supervisor and participate in cutting-edge projects on theoretical computer science to carry out research-based activities. The course aims to get students involved in the latest development of theoretical computer science. It will cultivate a better understanding of the theory and applications among students and give them the opportunity to publish papers on their respective research practices. In this course, students are required to take part in formal presentations on research practices, including thesis proposal, mid-term and final defenses.

Instructor: Longbo Huang

This course aims at giving a comprehensive introduction to the fundamentals of computer networks and network performance analysis. The course contains two parts. The first part covers various networking topics including network principles, Ethernet, WiFi, routing, inter-networking, transport, WiMax and LTE, QoS, and physical layer knowledge. The second part presents mathematical techniques for modeling, analyzing and designing computer systems, including convex optimization, queueing theory, game theory and stochastic analysis. This course is intended for junior or senior undergraduate students in computer science or electrical engineering.

Instructor: Xiongfeng Ma

This course is offered to upper level undergraduate students, junior or senior students in the Yao Class, physics, EE, and computer science departments. The course will cover topics at the forefront of the new field of quantum communication and cryptography, including, for instance, foundation of quantum information, quantum entanglement, quantum cryptography, quantum communication, quantum random number generation, physical implementation of quantum communication and networks. The goal is to help the future researchers to find the interesting topics to work on.

Instructor: Chongjie Zhang

This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. Specific topics include search, constraint satisfaction, game playing, graphical models, machine learning, Markov decision processes, and reinforcement learning. The main goal of the course is to equip students with the tools to tackle new AI problems you might encounter in life and also to serve as the foundation for further study in any AI area you choose to pursue.

Instructor: Yihan Gao

This course offers a broad coverage of topics in the field of data mining. The first half of the course cover basic data mining concepts including: data preparation, knowledge presentation, classification, clustering, generalization of algorithms, evaluation of credibility, and association analysis. The second half of the course covers some of the more advanced research topics in the field of data mining. This course intends to be a first course on data mining that prepares students for further study, which introduces students to many different topics so that they can pursue their favorite ones on their own after the course.

Instructor: Kaisheng Ma

This is a course focusing both on theoretical and experimental hardware fundamentals. The target is to implement small scale convolution operation in CNN on FPGA. After the course, students should be able to handle: How to divide control logics and computing logics. How to implement logics, timing, state-machine etc. Able to make testbenches. Able to map to FPGA, and debug on it. Know basics about back-end about ASIC chip design, like verification, layout etc. Able to implement a 3*3 convolution layer, and finish the local memory, global memory.