This course examines signals, systems and inference as unifying themes in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; group delay; state feedback and observers; probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization; least-mean square error estimation; Wiener filtering; hypothesis testing; detection; matched filters.

This course examines why democracy emerges and survives in some countries rather than in others; how political institutions affect economic development; and how American politics compares to that of other countries. It reviews economic, cultural, and institutional explanations for political outcomes. It also includes case studies of politics in several countries. Assignments include several papers of varying lengths and extensive structured and unstructured class participation.

This course introduces the basic computational methods used to understand the cell on a molecular level. It covers subjects such as the sequence alignment algorithms: dynamic programming, hashing, suffix trees, and Gibbs sampling. Furthermore, it focuses on computational approaches to: genetic and physical mapping; genome sequencing, assembly, and annotation; RNA expression and secondary structure; protein structure and folding; and molecular interactions and dynamics.

This course gives a mathematical introduction to neural coding and dynamics. Topics include convolution, correlation, linear systems, game theory, signal detection theory, probability theory, information theory, and reinforcement learning. Applications to neural coding, focusing on the visual system are covered, as well as Hodgkin-Huxley and other related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission.
Visit the Seung Lab Web site.

This is an introductory course on computational thinking. We use the Julia programming language to approach real-world problems in varied areas, applying data analysis and computational and mathematical modeling. In this class you will learn computer science, software, algorithms, applications, and mathematics as an integrated whole. Topics include image analysis, particle dynamics and ray tracing, epidemic propagation, and climate modeling.

This class uses revolutionary programmable interactivity to combine material from three fields – Computer Science + Mathematics + Applications – creating an engaging, efficient learning solution to prepare students to be sophisticated and intuitive thinkers, programmers, and solution providers for the modern interconnected online world.
Upon completion, students are well trained to be scientific “trilinguals,” seeing and experimenting with mathematics interactively as math is meant to be seen, and ready to participate and contribute to open source development of large projects and ecosystems.

6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

This half-semester course introduces computational thinking through applications of data science, artificial intelligence, and mathematical models using the Julia programming language. This Spring 2020 version is a fast-tracked curriculum adaptation to focus on applications to COVID-19 responses.
See the MIT News article Computational Thinking Class Enables Students to Engage in Covid-19 Response

6.00 Intro to CS and Programming has been retired from OCW. You can access the archived course on DSpace – MIT’s digital repository. Please see the list of introductory programming courses and other programming courses from recent years.

6.0001 Introduction to Computer Science and Programming in Python is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

This course presents the fundamentals of object-oriented software design and development, computational methods and sensing for engineering, and scientific and managerial applications. It cover topics, including design of classes, inheritance, graphical user interfaces, numerical methods, streams, threads, sensors, and data structures. Students use Java® programming language to complete weekly software assignments.
How is 1.00 different from other intro programming courses offered at MIT?
1.00 is a first course in programming. It assumes no prior experience, and it focuses on the use of computation to solve problems in engineering, science and management. The audience for 1.00 is non-computer science majors. 1.00 does not focus on writing compilers or parsers or computing tools where the computer is the system; it focuses on engineering problems where the computer is part of the system, or is used to model a physical or logical system.
1.00 teaches the Java programming language, and it focuses on the design and development of object-oriented software for technical problems. 1.00 is taught in an active learning style. Lecture segments alternating with laboratory exercises are used in every class to allow students to put concepts into practice immediately; this teaching style generates questions and feedback, and allows the teaching staff and students to interact when concepts are first introduced to ensure that core ideas are understood. Like many MIT classes, 1.00 has weekly assignments, which are programs based on actual engineering, science or management applications. The weekly assignments build on the class material from the previous week, and require students to put the concepts taught in the small in-class labs into a larger program that uses multiple elements of Java together.

Second of two modules facilitating a basic understanding of computing in planning and public management. Students develop problem-solving skills using computer-based tools for “what-if” analyses. Emphasis on spatial analysis using geographic information systems and database query tools.

This course studies representative twentieth and twenty-first-century texts and films from Hispanic America and Spain. Emphasis is on developing strategies for analyzing the genres of the novel, the short story, the poem, the fictional film, and the theatrical script. The novels read this semester are Magali García Ramis’s Felices días, Tío Sergio (1986, Puerto Rico) and Javier Cercas’s Soldados de Salamina (2001, Spain). We will study Lorca’s play “La casa de Bernarda Alba” (1936, Spain), films from Spain, México, and Cuba, poems by Darío (Nicaragua), Machado (Spain), Lorca (Spain), Hernández (Spain), Vallejo (Perú), Cernuda (Spain), and Luis Palés Matos (Puerto Rico), and short stories from México (by an exiled Spanish writer), Chile, Argentina, and Cuba. Thematic emphasis is on the Spanish Civil War, changing attitudes toward gender, the Spanish-speaking Caribbean, and the history of race in the Americas.

This course studies important twentieth century texts from Spain and Latin America. The readings include short stories, theatre, the novel and poetry. This subject is conducted in Spanish and all reading and writing for the course is also done in Spanish.

This course aims to give students the tools and training to recognize convex optimization problems that arise in scientific and engineering applications, presenting the basic theory, and concentrating on modeling aspects and results that are useful in applications. Topics include convex sets, convex functions, optimization problems, least-squares, linear and quadratic programs, semidefinite programming, optimality conditions, and duality theory. Applications to signal processing, control, machine learning, finance, digital and analog circuit design, computational geometry, statistics, and mechanical engineering are presented. Students complete hands-on exercises using high-level numerical software.
Acknowledgements
The course materials were developed jointly by Prof. Stephen Boyd (Stanford), who was a visiting professor at MIT when this course was taught, and Prof. Lieven Vanderberghe (UCLA).

This course is an introduction to copyright law and American law in general. Topics covered include: structure of federal law; basics of legal research; legal citations; how to use LexisNexis®; the 1976 Copyright Act; copyright as applied to music, computers, broadcasting, and education; fair use; Napster®, Grokster®, and Peer-to-Peer file-sharing; Library Access to Music Project; The 1998 Digital Millennium Copyright Act; DVDs and encryption; software licensing; the GNU® General Public License and free software.

This is MIT’s introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), and we’ll try to explain everything else along the way! Experience in Python is helpful but not necessary.

This course will introduce students to architectural design and computation through the use of computer modeling, rendering and digital fabrication. The course focuses on teaching architectural design with CAD drawing, 3-D modeling, rendering and rapid prototyping. Students will be required to build computer models that will lead to a full package of architectural explorations with computers. Each semester we will explore the design process of a particular building type and building material.
The course also investigates a few design processes of selected architects. The course is critical of design principles and building production methods. Student assignments are graded based on the quality of design, representation and constructability. Great design input is always encouraged.

This subject explores the varied nature and practice of computation in design. We will view computation and design broadly. Computation will include both work done on the computer (digital computing) and by-hand. Design will include both the process of making designs and artifacts, as well as the designs and artifacts themselves. The aim of the course is to develop a view of computation and design beyond the specifics of techniques and tools, and a critical, self-awareness of our own approaches and metaphors for computation and design.

Introduction to Design Inquiry explores the nature and exercise of design intelligence. It aims to open avenues for further research and, along them, to open vistas on the teaching of design and on more mindful professional design practices.
We see design as processes located in individuals and groups, shaped by the formation and experience of each individual and by the characteristics of the groups that play a role in the design process. People construct the worlds they inhabit out of what they know and have experienced. So also does the designer, but the designer’s worlds must be possible for others to inhabit and, therefore, to construct. Indeed the success of a design depends in large part on the degree to which these constructive processes yield similar results.