Topics in Mathematics with Applications in Finance

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 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.

_Project Laboratory in Mathematics_ immerses students in the world of mathematical research, allowing them to collaboratively tackle complex problems and present their findings. This course emphasizes teamwork and the exploration of mathematical concepts through hands-on projects.

We will talk about the contents covered in general statistics lectures in a storytelling manner. You can use R, a free program, as easily as Excel, and easier than SPSS.

A reintroduction to high school mathematics that starts with nothing more than your memory of middle school math. The course is reorganized according to the causal relationships between concepts rather than the textbook order, so you first understand why each formula is needed. Designed to be completed in 24 weeks with 30 minutes a day, it helps working professionals and adult learners build a solid foundation in the mathematical concepts they encounter at work and in everyday life.

For those of you who are having a hard time with college statistics, a senior who was in the same position will share his know-how. Conquering basic statistics using R programming

This course is for those who want to study machine learning but lack statistical knowledge.

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.