Computer Science

This course combines foundational machine learning concepts with hands-on competitive challenges, preparing students to become effective data scientists in collaborative international environments. The class will be assigned to mixed teams of international and domestic students, tackling real-world datasets through Kaggle-style competitions. 

This course aims to provide students with foundational knowledge in artificial intelligence, with a particular focus on the latest advancements in Large Language Models (LLMs), to help them understand core concepts and application scenarios of AI technology.

The course introduces information technology through the web as a core case study. It examines the hardware, software, networks, and operating systems that support websites and develops the knowledge and skills required to design and build a functional website.

Through this course, student will understand fundamental principle and theory of database system, be able to do practical applications, learn about the new advance in data management techniques. This course covers topics including relational model, SQL language, ER model, relational database design, introduction to the transaction system and database performance tuning.

Search engines and recommendation systems are among the primary tools for information acquisition by internet users, with their design and implementation integrating the highest level of research achievements in today's internet application field. By studying this course, students can not only gain a solid understanding of information retrieval, data mining, natural language processing, and machine learning—areas closely linked to the practical applications of the internet—but also enhance their ability to apply this knowledge comprehensively to solve problems.

This course is a journey into the essence of computation. Using rigorous mathematical language, the course unveils the deep logic underlying computer science. Following the theme of “Logic and Computation,” it guides you through the intellectual landscape of Turing machines, calculus, time and space complexity, zero-knowledge proofs, and the interplay between logic and computation.

This course is an introduction to the analysis of algorithms and data structures. It covers common abstract data types and their implementations, asymptotic complexity analysis, sorting and searching algorithms, depth-first and breadth-first search and applications, and graph optimization problems.

This course will briefly summarize the fundamental knowledge of linear algebra, statistical probability and distribution theory, python programming language and databases. In addition, this course will introduce the main workflow and concepts in machine learning, including supervised learning, unsupervised learning, regression, classification and some mainstream algorithms. Importantly, some representative examples of machine learning in material science will be introduced. Meanwhile, the importance of quantum chemistry and molecular dynamics simulations in machine learning will be emphasized at the end.

This course focuses on utilizing a computer to master mathematical skills. Starting with the basic usage of Octave (or MATLAB), the class instructs on how one can solve various mathematical problems by writing and executing simple programs. 

Computer science is a broad ranging and diverse discipline, with many distinct specialist areas. This course allows students to experience some of this breadth by allowing them to study two independent topics of their choice