Computational Music Theory and Analysis

The one-year real-world record of a company that declared an “AI all-in” strategy. This case study introduces how DeNA, one of Japan’s leading IT companies, transformed an organization of 3,000 people into an AI-native company. For organizations preparing for an AI transformation, we’ve gathered only the most useful insights.

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 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 enhances kanji learning through engaging video lectures covering kanji characters from Tobira lessons 6–10. Students will learn not only how to write kanji but also the fascinating stories behind each character.

This course explores the design and functionality of Bitcoin and other cryptocurrencies, emphasizing cryptography, game theory, and network architecture. Learners will engage with practical programming assignments to deepen their understanding of these evolving systems.

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.

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 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 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 explores the application of autonomy in real-world systems through the programming of cognitive robots. Learners will study advanced reasoning techniques and algorithms that enable robots to monitor themselves, schedule tasks, and collaborate with other agents.

This course is an introduction to the process of generating a symbolic description of the environment from an image. It covers the physics of image formation, image analysis, binary image processing, and filtering, with applications in robotics and intelligent machine interaction.

This course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines such as physics, economics, social sciences, natural sciences, and engineering. It parallels the combination of theory and applications in Professor Strang’s textbook Introduction to Linear Algebra.

This course introduces machine learning in the medical field, covering the characteristics of clinical data and learning about risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and clinical workflow improvement through machine learning.

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 introduces the core theory of modern financial economics and financial management, focusing on capital markets and investments. Learners will explore asset valuation, capital budgeting, portfolio selection, and the efficient market hypothesis, among other key topics.

This course introduces the dynamics and vibrations of lumped-parameter models in mechanical systems, focusing on kinematics, force-momentum formulations, and Lagrange's equations. Learners will also explore numerical methods and MATLAB® applications to solve dynamics and vibrations problems.

This course explores the transformative potential of blockchain technology in the realms of money and finance. Learners will study the fundamentals of blockchain, distributed ledgers, and smart contracts, with a focus on current and future applications in the financial sector.

This course explores the mathematical concepts and techniques essential for the financial industry, blending theoretical mathematics with practical applications. Learners will study topics such as linear algebra, probability, statistics, and stochastic processes, guided by both MIT mathematicians and industry professionals.

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

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 is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

We will help you easily understand the core concepts of machine learning and acquire the ability to implement practical machine learning applications by moving away from theory-based machine learning courses.

This course builds on the foundations of programming in Python, emphasizing computational thinking and data science principles. Learners will explore how computation can effectively solve real-world problems, empowering them to write functional programs for various applications.

This course is ideal for those who want to focus on theory and context rather than practice, aiming to organize their knowledge of deep learning in depth and grasp the big picture. It makes the mathematical and statistical foundations of deep learning easy to understand and provides intuitive interpretations of key modern models—such as AutoEncoder, GAN, Transformer, and AlphaGo—from a developer's perspective.

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.

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.