Introduction to Neural Computation

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 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 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 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 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.

Functional analysis explores the study and resolution of linear and nonlinear problems in normed spaces, extending beyond finite dimensions. Learners will delve into key concepts such as normed spaces, completeness, functionals, and the Spectral Theorem, gaining insights applicable to various mathematical and physical contexts.

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 offers a hands-on, project-based introduction to building scalable and high-performance software systems. Learners will explore performance analysis, algorithmic techniques, and optimizations to effectively utilize modern computing platforms.

This course is intended for students with little to no programming experience and focuses on understanding the role of computers in problem-solving. Students will build confidence in writing simple programs using the Python 3 programming language.

This graduate course delves into the fundamental principles of Einstein's general theory of relativity, exploring topics such as differential geometry, black holes, and cosmology. Learners will engage with both the theoretical foundations and practical applications of general relativity.

This course covers quantum physics including applications of modern physics, and explores topics such as the general formalism of quantum mechanics, the harmonic oscillator, three-dimensional quantum mechanics, angular momentum, spin, and the addition of angular momentum.

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 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 focuses on the practical aspects of designing and analyzing non-digital games. Learners will explore various game genres while collaborating in teams to create, develop, and test original games, gaining insights into game mechanics and rules.

This course provides an introduction to cellular and population-level systems biology with an emphasis on synthetic biology, modeling of genetic networks, cell-cell interactions, and evolutionary dynamics. Learners will explore genetic switches, network motifs, and models of pattern formation.

This course covers the foundations of mathematical analysis. You will study the convergence of sequences and series, continuity, differentiability, Riemann integration, sequences and series of functions, uniformity, and the interchange of limit operations.

This course covers the experimental foundations of quantum physics. It introduces wave mechanics and one-dimensional and three-dimensional solutions to the Schrödinger equation.

This course focuses on computability and computational complexity theory. Learners will cover topics such as regular languages, context-free languages, decidable and undecidable problems, and recursive function theory.

This course covers the basics of electrical engineering and electronic circuits, teaching the fundamental principles required for electronic system design. Students will explore various topics, including resistive elements, switches, MOS transistors, and analog and digital circuits.

This course surveys questions about human behavior and mental life, exploring topics such as perception, love, consciousness, and the nature of self. Students will engage with various theoretical perspectives, including biological, cognitive, and psychoanalytic, while experiencing psychology firsthand through demonstrations and discussions.

This course introduces various approaches to studying animal behavior. You will learn key concepts of animal behavior, including learning, habitat selection, and reproductive behavior.

This course provides an introduction to cellular and population-level systems biology with an emphasis on synthetic biology, modeling of genetic networks, cell-cell interactions, and evolutionary dynamics. Learners will explore genetic switches, network motifs, and models of pattern formation.

This course examines the neural bases of sensory perception, focusing on the physiological and anatomical studies of the mammalian nervous system alongside behavioral studies of animals and humans. Learners will explore topics such as visual pattern recognition, color and depth perception, auditory responses, and somatosensory perception.

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.