Introduction to Modeling and Simulation

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 is an introductory exploration of documentary film theory and production, focusing on documentaries about science, engineering, and related fields. Students engage in digital video production as well as social and media analysis of science documentaries.

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 provides a comprehensive overview of the fundamental principles of biochemistry, genetics, molecular biology, and cell biology, with a specific focus on their applications in understanding human biology. Students will explore topics such as disease, developmental biology, neurobiology, and evolution, emphasizing biological functions at the molecular level.

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 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 formation of chemical elements from the Big Bang to the life cycles of stars. Learners will study how elements like carbon and iron originate in the cosmos and their significance in the universe.

This course provides a fast-paced introduction to deep learning, focusing on practical model-building for complex problems involving unstructured data. Learners will explore deep neural networks, convolutional networks, transformers, and generative models like ChatGPT and Midjourney.

This course examines the chemical and physical properties of the cell and its building blocks, with a focus on protein structures and catalysis principles. Learners will explore metabolic pathways, including glycolysis and the Krebs cycle, to understand the chemistry behind biological processes.

This course connects algorithmic puzzles with computer programming, teaching students to code while solving engaging challenges. Learners will explore Python syntax and semantics as they tackle various puzzles, enhancing their programming skills in a fun and practical way.

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 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 Reflective Practice approach developed by Donald Schön, focusing on how professionals can effectively combine their knowledge with practical situations. Learners will explore theories of learning and innovation to enhance their cognitive resources and improve their professional practice.

This capstone course focuses on integrating various disciplines of nuclear physics and engineering to tackle real-world design challenges in nuclear applications. Learners will engage in a group project to design a power plant that emphasizes emission-free electricity generation, carbon sequestration, and fossil fuel displacement.

This course introduces skills and capabilities for real-world problem-solving and taking technology from lab to societal impact. Innovation Teams is MIT’s original deep technology to impact innovation factory, the longest-running lab-to-market class at MIT, and is a collaboration between the School of Engineering and the Sloan School of Management.

This course covers the essential steps to start a new venture, whether for-profit or social. Learners will explore the journey from idea inception to launching a successful business, including customer identification, business models, financial projections, and more.

This course focuses on developing literacy skills in Chinese for learners with basic conversational abilities. Students will engage in reading and writing in both full and simplified characters, enhancing their everyday language usage.

This course provides practical instruction in the design and analysis of non-digital games. Students cover the texts, tools, references and historical context to analyze and compare game designs across a variety of genres, including sports, game shows, games of chance, card games, schoolyard games, board games, and role–playing games.

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 covers the fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high dimensions, and applications to computer vision, natural language processing, and robotics.

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 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 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 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 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.

This course covers fundamental discrete mathematics for computer science and engineering. It focuses on mathematical definitions, proofs, and applicable methods, covering various topics such as logic symbols, proof techniques, sets, relations, and graph theory.

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 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 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 emphasizes dynamic models of growth and development, exploring topics such as migration, modernization, and technological change. Learners will study the interplay between economic theory and real-world applications in developing countries, focusing on income distribution, institutional change, and the functioning of financial markets.

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.