1. Machine Learning and Translation

Instructor:

This course is set for graduate and senior undergraduate students, who have a basic background in statistics.

The course teaches the fundamental theory and algorithms behind machine learning (ML), a key issue in the artificial intelligence. It aims to equip students with a deeper understanding and adept skills in the field of ML, to solve various pragmatic questions in broader areas. ML has a wide range of applications, including web search, social network, speech recognition, image processing, robotics and finance. We choose the application of statistical machine translation to demonstrate how the ML study is motivated and applied. Machine translation is one of the hottest area nowadays, with governments and large companies such as Microsoft, Baidu and Google investing huge resources to the task.

The syllabus contains both theory and practice: First, we describe the training criteria (Bayesian decision rule, maximum likelihood), statistical models (CRF, SVM, HMM and neural network) and training algorithms (EM and Gibbs Sampling); Then, advanced machine translation methods are introduced based on the statistical approaches (word alignment, phrase training, decoding and optimization).

The course is mainly conducted through lectures and discussions. The students are encouraged to join research projects to realize and to develop novel ideas.

2. Introduction to Computational Theory

Instructor:

This course is set for junior graduate students, who are expected to have good understandings of mathematics and knowledge in basic theoretical computer science.

The course introduces main contents of computational theory with an emphasis on important topics concerning modern and contemporary complexity theories, which enables students to know major issues and results in the field of complexity theories. It also helps students to determine their future research interests through understandings of algorithm.

Topics covered in the course are: review of main contents of computability theory, introduction of basic themes of complexity theory, including basic complexity classes like P, NP, PSPACE and BPP; proof of the non-existence of circuit and Parity in AC0; Hierarchy Theorems of time and space; main results of derandomization; PCP theorem and non-approximatability; theoretical coding; and etc.

The course is mainly conducted through lectures and series seminars, supplemented by featured discussions. The students are required to take thesis reading exercises and give summary reports, with a view to helping them find their future research interests.

3. Algorithm Analysis and Design

Instructor:

This course is set for junior graduate students, who are expected to have good understandings of mathematics and knowledge in basic theoretical computer science.

The course introduces advanced technologies concerning the design and analysis of algorithm, as students will read theses in the field of algorithm design. It also helps students to determine their future research interests through understandings of algorithm.

Topics covered in this course are: review of basic technologies of algorithm design including divide-and-conquer algorithms and dynamic programming; introduction of the design and analysis of random algorithm and approximation algorithm; introduction of the current research on important issues including linear algorithm, online algorithm, and data structure in computational geometry.

The course is mainly conducted through lectures and series seminars, supplemented by featured discussions. The students are required to take thesis reading exercises and give summary reports, with a view to helping them find their future research interests.

4. Advanced theoretical computer science(2)

Instructor:

This course is set for junior graduate students who are interested in theoretical computer science and relevant disciplines. Those registered for this course are expected to have sound foundation of mathematics and knowledge in fundamental theoretical computer science. (Basis of Mathematics refers to College Mathematics, Basic Algebraic Theories, and etc. while fundamental theoretical computer science knowledge includes basis of algorithm design and complexity theory, and etc.)

Lectures for this course, all in English, aim to introduce current research directions, latest development and hot topics in computer science, and carry out in-depth discussions on issues of common interest. This course will help students to determine their future research interests and goals through featured discussions.

The teaching contents include various directions of classical theoretical computer science, such as design of algorithm, computational complexity theory, cryptography, game theory, coding theory, and quantum computing, etc.; as well as some major problems, hot topics and frontiers in the field of computer science, such as computational biology, compressive sensing network, network coding theory, and computer vision.

The course is mainly conducted through lectures and series seminars, supplemented by featured discussions. The students are required to take thesis reading exercises and give summary reports, with a view to helping them find their future research interests.

5. Advanced theoretical computer science(1)

Instructor:

This course is set for junior graduate students who are interested in theoretical computer science and relevant disciplines. Those registered for this course are expected to have sound foundation of mathematics and knowledge in fundamental theoretical computer science. (Basis of Mathematics refers to College Mathematics, Basic Algebraic Theories, and etc. while fundamental theoretical computer science knowledge includes basis of algorithm design and complexity theory, and etc.)

Lectures for this course, all in English, aim to introduce current research directions, latest development and hot topics in computer science, and carry out in-depth discussions on issues of common interest. This course will help students to determine their future research interests and goals through featured discussions.

The teaching contents include various directions of classical theoretical computer science, such as design of algorithm, computational complexity theory, cryptography, game theory, coding theory, and quantum computing, etc.; as well as some major problems, hot topics and frontiers in the field of computer science, such as computational biology, compressive sensing network, network coding theory, and computer vision.

The course is mainly conducted through lectures and series seminars, supplemented by featured discussions. The students are required to take thesis reading exercises and give summary reports, with a view to helping them find their future research interests.