Computer Systems Security

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 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 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 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 course delves into complex variables, differential equations, and linear algebra, providing a comprehensive understanding of these essential mathematical concepts. Learners will explore foundational theories and applications that are crucial for advanced studies in mathematics and engineering.

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 the modeling and analysis of random phenomena and processes, emphasizing statistical inference and probabilistic thinking. Learners will explore essential models and tools that combine mathematical rigor with conceptual understanding.

This course introduces students to the mathematical concepts and techniques essential in the financial industry, blending theoretical mathematics with practical applications. Learners will explore topics such as probability, statistics, and linear algebra, all within the context of finance.

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 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 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 explores the interplay between social behavior, cognition, and the concept of design in nature, as influenced by Darwin's theories. Learners will engage with historical and contemporary texts that examine the implications of design without a designer, focusing on both human and non-human social animals.

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 quantitative approaches to understanding brain and cognitive functions. Learners will explore mathematical descriptions of neurons, their responses to stimuli, and foundational data analysis tools in neuroscience.

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 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 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 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 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 course delves into complex variables, differential equations, and linear algebra, providing a comprehensive understanding of these essential mathematical concepts. Learners will explore foundational theories and applications that are crucial for advanced studies in mathematics and engineering.

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 the modeling and analysis of random phenomena and processes, emphasizing statistical inference and probabilistic thinking. Learners will explore essential models and tools that combine mathematical rigor with conceptual understanding.

This course introduces students to the mathematical concepts and techniques essential in the financial industry, blending theoretical mathematics with practical applications. Learners will explore topics such as probability, statistics, and linear algebra, all within the context of finance.

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 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 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 explores the interplay between social behavior, cognition, and the concept of design in nature, as influenced by Darwin's theories. Learners will engage with historical and contemporary texts that examine the implications of design without a designer, focusing on both human and non-human social animals.

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 quantitative approaches to understanding brain and cognitive functions. Learners will explore mathematical descriptions of neurons, their responses to stimuli, and foundational data analysis tools in neuroscience.

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 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.