Introduction to Computational Thinking and Data Science

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 explores the fundamental role of data structures in computer science, emphasizing their importance in developing efficient algorithms. Learners will study advanced topics, including dynamic graphs, memory hierarchy, and hashing, while engaging with current research directions in the field.

This course focuses on the design and implementation of secure computer systems, exploring various threat models and security techniques. Learners will engage with topics such as operating system security, network protocols, and hardware security, drawing insights from recent research papers.

This course delves into the intricacies of high-level programming language implementation, focusing on compiler design and optimization techniques. Learners will engage in a hands-on project that involves building a compiler in Java® that generates MIPS executable machine code.

This course provides a comprehensive introduction to systems engineering, focusing on both classical and modern approaches. Learners will explore key concepts such as stakeholder analysis, requirements definition, system architecture, and design optimization, applying these principles to real-world projects.

This course delves into the essential concepts of matrix calculus, focusing on its applications in machine learning and large-scale optimization. Learners will explore techniques to understand and compute derivatives of matrices and their factorizations, enhancing their ability to tackle complex mathematical operations in modern computing.

This course explores the intricacies of poker theory and the application of poker analytics in investment management and trading. Learners will delve into decision-making frameworks and strategies that can enhance their understanding of both poker and financial environments.

This course explores the evolution of proofs in computer science, focusing on interactive proofs, multi-prover interactive proofs, and probabilistically checkable proofs. Learners will discover how to leverage cryptography to transform these advanced proof systems into computationally sound non-interactive arguments (SNARGs).

This course offers an immersive experience in the Japanese language, focusing on the simultaneous development of listening, speaking, reading, and writing skills. Learners will engage with authentic Japanese media to deepen their understanding of the language and culture.

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 explores the practical aspects of proving that certain problems cannot be solved efficiently, focusing on reductions and techniques for demonstrating computational hardness across various complexity classes. Learners will engage with interesting problems and develop a deeper understanding of algorithmic limitations.

This class provides some answers to basic questions about the nature of human language. Throughout the course, learners will examine how human language functions as a complex yet law-governed mental system, exploring topics such as language acquisition, historical language changes, and language endangerment.

This course focuses on computability and computational complexity theory. Learners will cover topics such as regular languages, context-free languages, decidable and undecidable problems, and recursive function theory.

This course covers elementary discrete mathematics for science and engineering, focusing on mathematical tools and proof techniques useful in computer science. Learners will explore topics such as logical notation, sets, relations, graph theory, state machines, and algorithms.

This course explores how financial technology, or FinTech, is transforming the financial services industry through innovative technologies. Learners will study key trends such as AI, blockchain, and open APIs, gaining insights into the evolving market landscape and regulatory environment.

This course covers key topics in discrete applied mathematics, focusing on counting, generating functions, probability, and more. Learners will engage with both mathematical concepts and effective communication through writing assignments.

This course delves into the fundamental principles of front-end processes essential for fabricating devices in silicon integrated circuits. Learners will explore advanced physical models and practical aspects of key processes such as oxidation, diffusion, ion implantation, and epitaxy, alongside emerging materials like Silicon Germanium (SiGe).

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 delves into the critical issues of bias and fairness in machine learning, focusing on their implications for international development. Learners will explore strategies for the appropriate use of ML to promote equity and social justice.

This course presents an innovative approach to teaching linear algebra, focusing on key concepts and their interconnections. Learners will explore foundational topics and gain insights into the structure and applications of linear algebra through engaging video content.

This course covers the fundamental principles of statistical mechanics, which takes a probabilistic approach to the equilibrium properties of large degrees of freedom. You will study various topics including thermodynamics, probability theory, and classical statistical mechanics.

This course reviews key genomic technologies and computational approaches that are transforming prognostics, diagnostics, and treatment in medicine. Learners will explore the intersection of genomics and patient care, including the ethical and social implications of personalized medicine.

This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. Learners will study essential concepts in probability and statistics, along with modern data analysis techniques such as regression, econometrics, and data visualization.

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Functional analysis explores the study and resolution of linear and nonlinear problems in normed spaces, extending beyond finite dimensions. Learners will delve into key concepts such as normed spaces, completeness, functionals, and the Spectral Theorem, gaining insights applicable to various mathematical and physical contexts.

This course introduces major approaches to symbol-based music theory and musicology. Learners will explore various topics, including music theory algorithms, music generation, and machine learning.

Understand the essence of data visualization as a balance between scientific accuracy and aesthetic expression, and systematically learn everything from color and symbology principles to graph design. By utilizing visual attributes based on cognitive science principles, you will complete your data communication skills to achieve reliability and persuasiveness in professional practice.

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This course covers numerical methods essential for solving complex problems in chemical engineering, including heat and mass transfer, fluid mechanics, and molecular simulation. Learners will explore various computational techniques and their applications in real-world engineering scenarios.

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.

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Learn multi-level data analysis using Structural Equation Modeling (SEM) bootstrapping techniques and Hierarchical Linear Modeling (HLM). This is an advanced course designed to help you grow into a statistical expert capable of analyzing complex research data, covering everything from advanced AMOS features to the practical application of HLM. You will master both theory and practice through real-world research cases in the fields of education, psychology, sociology, and health.

This course covers the basics of signals and systems analysis, focusing on methods for representing discrete-time and continuous-time signals, as well as linear time-invariant systems. Students can apply these theories to various application fields such as feedback, control, communications, and signal processing.

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This course explores the intricacies of poker theory and the application of poker analytics in investment management and trading. Learners will delve into decision-making frameworks and strategies that can enhance their understanding of both poker and financial environments.

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In the era of AI and Big Data, are you utilizing your organization's data effectively? This course builds essential capabilities for reading and interpreting data, covering everything from the foundations of statistical thinking to the core principles of machine learning and deep learning. Grow into a practical leader capable of discovering patterns amidst volatility and making data-driven decisions.

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

Let's cultivate professional expertise in programming, algorithms, and statistical analysis!