Probabilistic Methods in Combinatorics

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 modeling and simulation techniques, focusing on continuum methods, atomistic and molecular simulation, and quantum mechanics. Learners will engage in hands-on training to apply these methods to significant engineering challenges.

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 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.

Feeling overwhelmed by where to start with complex data analysis? I will clearly guide you through how to utilize R and AI Tutors using the practical know-how I've built up.

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.

We will make the entire data analysis process more accessible using chatGPT and generative AI. While gaining a better understanding of the data analysis process, you can also learn how to apply AI in practical data science.

In this course, you will learn the mathematical principles and intuition of discrete stochastic processes and master how to create and analyze various models. You will explore discrete probability models that are useful in various fields such as engineering, physics, and biology.

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.

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.

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.

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.

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.

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.

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.

If you're wondering, "I did the user research... but is this result truly representative of all users?" then pay attention! We will show you exactly how to add persuasiveness to your designs based on data. ✅ We explain complex statistical terms in very simple language. ✅ We carefully introduce the 3 core hypothesis tests essential for UX practice, covering everything from principles to hands-on practice. ✅ We show you how to easily analyze user data using AI—without costs or hallucinations.

This is an intermediate algorithm course that focuses on efficient algorithm design and analysis techniques. Learners will master various algorithm design paradigms, such as divide and conquer, dynamic programming, and greedy algorithms.

The 65th Open House is about showing how data analysis is enriched by changing the type of variables. It mainly covers scoring, binning, and optimal scaling. The 66th Open House is about learning in detail about the concept and understanding of marginal effects and subgroup analysis.

ADsP Summary + Past Exam Solutions. Reflects August 2025 46th Exam! (Continuously Updated!)

As the popularity and interest in AB Testing explodes, we will introduce Korea's first AB Testing lecture right now!

ADsP Certification All-In-One Course! Ultra-Short Completion! Reference Book: "Everyone's ADsP Cheat Sheet" https://wikidocs.net/book/18829

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.