|No.||Semester and year||Course name||Credits||Instructor|
|1||Fall, Freshman||Introduction to Computer Science||3||Ran Duan|
|2||Fall, Freshman||Introduction to Artificial Intelligence||3||Yang Yuan, Yi Wu|
|3||Fall, Freshman||Linear Algebra||4||Yong Xu|
|4||Fall, Sophomore||Algorithm Design||4||Jian Li|
|5||Fall, Sophomore||General Physics(2)||4||Luyan Sun|
|6||Fall, Sophomore||Probability and Statistics||3||Yuhao Wang|
|7||Fall, Junior||Fundamentals of Cryptography||4||Wenfei Wu|
|8||Fall, Junior||Quantum Communication and Cryptography||3||Xiongfeng Ma|
|9||Fall, Junior||Machine learning||4||Yang Yuan|
|10||Fall, Junior||Advanced Computer Graphics||3||Shimin Hu|
|11||Fall, Junior||Artificial Intelligence: Principles and Techniques||3||Chongjie Zhang|
|12||Fall, Junior||Operating System||4||Wei Xu|
|13||Fall, Junior||Introduction to Computer Networks||3||Longbo Huang|
|14||Fall, Junior||Computational Biology||3||Jianyang Zeng|
|15||Fall, Junior||Data Mining||3||Yihan Gao|
|16||Fall, Junior||Introduction to Artificial Intelligence Chip：From Verilog to FPGA||3||Kaisheng Ma|
|17||Fall, Senior||Research Practice||15||Professors from IIIS, Tsinghua University|
Instructor: Ran Duan
Designed to appeal to a diverse audience, this course examines some of the fundamental ideas of the science of computing. Lectures and hands-on assignments cover a wide variety of topics such as hardware organization, the Internet, computer programming, limits of computing, and graphics. No prerequisite.
Instructor: Yang Yuan, Yi Wu
This course aims at providing freshmen students with a broad overview of the Artificial Intelligence field, including computer vision, robotics, reinforcement learning, AI systems, and AI algorithms, motivating them to study the field, and encouraging them to conduct indepth investigation on different areas of the field. It is a required course for freshmen students in the Special Artificial Intelligence Polit Class. Lectures will be given by leading experts in AI areas from both academia and industry.
Instructor: Yong Xu
Linear algebra finds wide applications in various fields, such as computer sciences, physics, mathematics and their interdisciplinary fields. This course introduces the basic concepts and techniques of linear algebra. It includes the study of matrices and their properties, linear transformations and vector spaces. Concrete topics include systems of linear equations, row reduction and Echelon form, vector equations, solution sets of a linear equation, linear independence, linear transformation, the matrix of linear transformation, matrix algebra, characterization of invertible matrices, determinants, subspaces, null spaces, column spaces, bases and dimension, rank, eigenvalues and eigenvectors, diagonalization, inner product, etc. By introducing the concepts through concrete examples, students will learn the basic concepts and methods of linear algebra, and their capacity to think from the linear algebra perspective will be systematically trained and enhanced.
Instructor: Jian Li
This course gives an introduction to the basics of algorithm, common algorithm design techniques, and the analysis of running time (complexity). The main contents include: tools of algorithm analysis, divide and conquer algorithms, dynamic programming, greedy algorithms etc. algorithm design techniques, and NP complete, randomized algorithms, approximation algorithms and other advanced topics.
Instructor: Luyan Sun
This course is a follow-up course of General Physics I and for undergraduate students with serious interests in physics and interdisciplinary sciences. The main focus of this course is to cover the most important topics in classical electrodynamics including electrostatics, magnetostatics, Maxwell’s equations for electromagnetic fields, and special relativity. This course will emphasize both basic concepts and solving practical problems. After completing this course, students are expected to gain a good understanding of basic classical electrodynamics.
Instructor: Yuhao Wang
Statistical methodsoffer a powerful toolkit to extract useful information from massive and noisy observational data. This course introduces studentstomodern statistical methods and their theoretical foundations in high-dimensional and nonparametric models. In this course, we will covermodern statistical methods developed over the past 20 years, analyzetheir asymptotic properties and probabilistic foundations, and show how these methods can be applied into real data applications. Selected topicsinclude:high-dimensional and nonparametric estimation, minimax lower bound, multiple hypothesis testing, semiparametric models.
Instructor: Wenfei Wu
The purpose of this unit is to introduce the basic concepts of modern cryptography. Two main issues related to the distribution of digital content are privacy of information and origin of data. After a short introduction about Algebra, privacy issues and solutions are discussed in the context of modern private-key and public-key cryptography. Next, we will review tools allowing authentication of digital content using hash function and digital signatures. The presented constructions are building blocks for designing secure systems and protocols for real-world applications. Attacks and security analysis of the cryptographic schemes and protocols will also be discussed.
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: Yang Yuan
Machine learning studies how computers can learn from experiences. Combining ideas from theoretical computer science and statistics, researchers have developed many learning methods and their applications to computer vision, bioinformatics, natural language processing etc. are highly successful. Machine learning theory addresses the fundamental problems in learning. It studies the power and theoretical limits of learning. The aim is to provide deep understand of learning and the guidance for the development of practical algorithms.
Instructor: Shimin Hu
This course introduces basic concepts, elements, algorithms and systems of computer graphics. The main contents include color model, illumination model (Phong model, Cook-Torrance model), phong shading and Gouraud shading, texture mapping, ray tracing, curve and surface modeling, solid modeling, geometry processing, etc.
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: Wei Xu
This course teaches the basic principles of operating systems: computer and operating system structures, mechanisms and policies, resource management, implementation of multitask systems, memory management, file systems, I/O subsystem and device management, communication and networking, protection and security. Students are expected to spend additional time to gain hands-on experience.
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: Jianyang Zeng
To introduce various computational problems for analyzing biological data (e.g. DNA, RNA, protein sequences, and biological networks) and the algorithms for solving these problems. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.
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.
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.