Matrix Calculus for Machine Learning and Beyond

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 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 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 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 examines the choices and constraints regarding sources and uses of energy by households, firms, and governments through a number of frameworks to describe and explain behavior at various levels of aggregation. Examples include a wide range of countries, scope, settings, and analytical approaches.

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 explores how financial technology, or FinTech, is transforming the financial services industry through innovative technologies. Learners will study key trends such as AI, blockchain, and open APIs, gaining insights into the evolving market landscape and regulatory environment.

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 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 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 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 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 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 explores the fundamental principles of artificial intelligence, focusing on both theoretical concepts and practical applications. Learners will study algorithms, machine learning techniques, and the ethical implications of AI technologies.

This course equips learners with the essential knowledge and skills for teaching science and engineering at the college level. Participants will explore innovative teaching methods, engage in active learning, and develop their own teaching practices through research and presentations.

This course provides an introduction to chemical oceanography, focusing on the composition and dynamics of seawater and the biogeochemical cycles that shape oceanic environments. Learners will explore key concepts such as reservoir models, nutrient cycling, and the impact of human activities on ocean chemistry.

This course explores the integration of architectural design within natural landscapes, focusing on the relationship between built environments and their ecological contexts. Learners will develop skills in analyzing landscape and climate to inform their design decisions through drawings and models.

This course focuses on developing innovative information systems tailored for resource-poor settings, aiming to enhance health outcomes and strengthen organizational infrastructures. Learners will explore the intersection of public health, technology, and collaborative ecosystems to address healthcare challenges in developing countries.

This course is an introduction to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality.

This course provides an in-depth exploration of advanced topics in artificial intelligence, focusing on machine learning algorithms and their applications. Learners will study key concepts, techniques, and tools to develop AI solutions for real-world problems.

Were you a math-phobe during your school days? It's said that math is important in AI, but do you feel lost on how to approach it? The reason math is important in AI is because math is used in the computational processes of AI (Machine Learning and Deep Learning). For easy explanations of differentiation, linear algebra, and probability and statistics to understand AI, it is structured with simple explanations using graphs (coordinates) rather than mathematical derivations.

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 is a lecture where you can easily learn important mathematical concepts used in games. People who are well-versed in mathematics take this knowledge for granted, so they cannot teach it assuming you know nothing. I explain from a beginner's perspective, truly assuming you are a non-major.

This course is a collection of tutorial videos that visually explore various topics in materials science and engineering. Learners will discover how to use the Wolfram Mathematica programming system to easily understand complex concepts.

Introducing AI entry-level study methods for undergraduates.

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 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 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.