Mathematics for Computer Science

This course delves into projection theory, exploring how a set behaves under various orthogonal projections. Learners will engage with both classical techniques and contemporary applications across multiple mathematical disciplines, including harmonic analysis and analytic number theory.

This course focuses on learning several rules to improve speaking skills in important situations. Learners can master effective presentation methods for various contexts, such as interviews, thesis defenses, and oral exams.

This course introduces the mathematical modeling of computational problems and the common algorithms, algorithmic paradigms, and data structures used to solve them. It emphasizes the relationship between algorithms and programming, and introduces basic performance measurement and analysis techniques.

This is a foundational chemistry course for students with a strong background in chemistry. It focuses on the basic principles of the electronic structure of atoms and molecules, thermodynamics, acid-base and redox equilibria, chemical kinetics, and catalysis.

This course covers the fundamental principles of finite element analysis for solving complex static and dynamic problems. Learners will learn how to implement finite element procedures for linear and nonlinear analysis.

This course focuses on the close analysis and criticism of a wide range of films, from early silent cinema to classic Hollywood genres and European and Japanese art cinema. Students will compare films from different eras and countries to interpret in-depth analyses and explore theoretical issues related to audiences.

This course covers the fundamental principles of biochemistry, genetics, molecular biology, and cell biology. It places a particular emphasis on understanding the role of microorganisms in ecosystems and human health.

This course introduces the fundamental principles of biochemistry, molecular biology, and genetics to understand the functions of living systems. Learners will acquire the knowledge necessary to understand human health and therapeutic interventions through examples from chemical biology and 21st-century molecular genetics.

This course provides an introduction to nuclear science and its engineering applications. It explains basic nuclear models, radioactivity, nuclear reactions, and kinematics, and covers the interaction of ionizing radiation with matter.

This course covers the foundations of calculus, primarily including content taught in introductory calculus courses for freshmen. It allows learners to review core concepts of calculus and provides useful materials even for students learning it for the first time.

This course covers the content necessary to understand and create machine learning algorithms, specifically deep learning and neural networks, through linear algebra concepts. It provides a comprehensive explanation of linear algebra, including applications in probability, statistics, and optimization.

This course emphasizes the structural principles of digital systems and introduces architectures commonly applied across various technologies. Learners will cover a wide range of topics, including multi-level implementation strategies, the definition and mechanisms of new primitive elements, and performance measurement.

This course covers the basics and applications of the latest artificial intelligence technologies. Learners can develop problem-solving skills through AI principles and various use cases.

This course provides an interactive introduction to discrete mathematics for computer science and engineering. Learners will study fundamental mathematical concepts, discrete structures, and discrete probability theory, developing the ability to apply these to various fields of computer science.

This calculus course covers the differentiation and integration of functions of a single variable, concluding with a brief discussion of infinite series. This course serves as the foundation for various scientific fields, including physics, engineering, and economics.

In this course, students learn the complexities of developing video games in small, multidisciplinary teams. They will master creative design and production methods, and learn how to design, develop, and thoroughly test their own digital games in collaboration with team members.

This course introduces the mathematical modeling of computational problems. It covers common algorithms, algorithmic paradigms, and the data structures used to solve these problems.

This is an intermediate algorithm course that focuses on efficient algorithm design and analysis techniques. Learners will master various algorithm design paradigms, such as divide and conquer, dynamic programming, and greedy algorithms.

This course covers differentiation, integration, and vector calculus for multivariable functions. These mathematical tools and methods are used extensively in the physical sciences, engineering, economics, and computer graphics.

This course covers the fundamental principles of biochemistry, genetics, molecular biology, and cell biology. In particular, you will learn about biological functions at the molecular level, as well as the structure and regulation of genes and proteins.

This course introduces the mathematical modeling of computational problems and the common algorithms, algorithmic paradigms, and data structures used to solve them. It emphasizes the relationship between algorithms and programming, and introduces basic performance measurement and analysis techniques.

This course focuses on algorithms for analyzing and designing geometric folds. Students will learn about the reconfiguration of foldable structures, linkages and origami, and the unfolding and folding of 3D polyhedra.

Through this course, you can systematically learn core concepts such as Basic Sorting Algorithms, Advanced Sorting Algorithms, Special Purpose Sorting Algorithms, and Practical Considerations.

Through this course, you can systematically learn core concepts such as basic tree concepts and data structures, search algorithms, tree application algorithms, advanced tree structures and applications, and practical problem-solving strategies.

This course covers the content necessary to understand and create machine learning algorithms, specifically deep learning and neural networks, through linear algebra concepts. It provides a comprehensive explanation of linear algebra, including applications in probability, statistics, and optimization.

This course introduces the mathematical modeling of computational problems. It covers common algorithms, algorithmic paradigms, and the data structures used to solve these problems.

The core of software development ultimately lies in 'how quickly data can be found and processed.' Among numerous algorithms, the Hash Table is an essential tool that forms the foundation of modern programming languages and system architectures. This lecture goes beyond simple theoretical explanations to cover how students can 'immediately' resolve specific performance bottlenecks encountered in practice using hash tables.

This is an intermediate algorithm course that focuses on efficient algorithm design and analysis techniques. Learners will master various algorithm design paradigms, such as divide and conquer, dynamic programming, and greedy algorithms.