Online courses directory (684)

This is a reading and discussion subject on issues in the engineering of software systems and software development project design. It includes the present state of software engineering, what has been tried in the past, what worked, what did not, and why. Topics may differ in each offering, but will be chosen from: the software process and lifecycle; requirements and specifications; design principles; testing, formal analysis, and reviews; quality management and assessment; product and process metrics; COTS and reuse; evolution and maintenance; team organization and people management; and software engineering aspects of programming languages.

Analyzes computational needs of clinical medicine reviews systems and approaches that have been used to support those needs, and the relationship between clinical data and gene and protein measurements. Topics: the nature of clinical data; architecture and design of healthcare information systems; privacy and security issues; medical expertsystems; introduction to bioinformatics. Case studies and guest lectures describe contemporary systems and research projects. Term project using large clinical and genomic data sets integrates classroom topics.

6.933J / STS.420J provides an integrated approach to engineering practice in the real world. Students of 6.933J / STS.420J research the life cycle of a major engineering project, new technology, or startup company from multiple perspectives: technical, economic, political, and cultural. Research involves interviewing inventors, reading laboratory notebooks, evaluating patents, and looking over the shoulders of engineers as they developed today's technologies. This subject is for students who recognize that technical proficiency alone is only part of the formula for success in technology.

This course provides a challenging introduction to some of the central ideas of theoretical computer science. Beginning in antiquity, the course will progress through finite automata, circuits and decision trees, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NP-completeness, the power of randomness, cryptography and one-way functions, computational learning theory, and quantum computing. It examines the classes of problems that can and cannot be solved by various kinds of machines. It tries to explain the key differences between computational models that affect their power.

Learn how to use Git for version control, managing a Drupal team and solid workflow practices.

This class explores sound and what can be done with it. Sources are recorded from students' surroundings - sampled and electronically generated (both analog and digital). Assignments include composing with the sampled sounds, feedback, and noise, using digital signal processing (DSP), convolution, algorithms, and simple mixing. The class focuses on sonic and compositional aspects rather than technology, math, or acoustics, though these are examined in varying detail. Students complete weekly composition and listening assignments; material for the latter is drawn from sound art, experimental electronica, conventional and non-conventional classical electronic works, popular music, and previous students' compositions.

This course examines classical and quantum models of electrons and lattice vibrations in solids, emphasizing physical models for elastic properties, electronic transport, and heat capacity. Topics covered include: crystal lattices, electronic energy band structures, phonon dispersion relatons, effective mass theorem, semiclassical equations of motion, and impurity states in semiconductors, band structure and transport properties of selected semiconductors, and connection of quantum theory of solids with quasifermi levels and Boltzmann transport used in device modeling.

This course is an introduction to quantum computational complexity theory, the study of the fundamental capabilities and limitations of quantum computers. Topics include complexity classes, lower bounds, communication complexity, proofs, advice, and interactive proof systems in the quantum world. The objective is to bring students to the research frontier.

I quick set of videos that show you how to use phpMyAdmin to build an online MySQL database.

6.270 is a hands-on, learn-by-doing class, in which participants design and build a robot that will play in a competition at the end of January. The goal for the students is to design a machine that will be able to navigate its way around the playing surface, recognize other opponents, and manipulate game objects. Unlike the machines in Design and Manufacturing I (2.007), 6.270 robots are totally autonomous, so once a round begins, there is no human intervention.

The goal of 6.270 is to teach students about robotic design by giving them the hardware, software, and information they need to design, build, and debug their own robot. The subject includes concepts and applications that are related to various MIT classes (e.g. 6.001, 6.002, 6.004, and 2.007), though there are no formal prerequisites for 6.270.

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

6.301 is a course in analog circuit analysis and design. We cover the tools and methods necessary for the creative design of useful circuits using active devices. The class stresses insight and intuition, applied to the design of transistor circuits and the estimation of their performance. We concentrate on circuits using the bipolar junction transistor, but the techniques that we study can be equally applied to circuits using JFETs, MOSFETs, MESFETs, future exotic devices, or even vacuum tubes.

If you are a student wanting to learn C programming, or an adult learner simply researching C programming courses, this free introductory course is for you.<br /><br />The C programming language is one of the most popular and widely used programming languages. It is a general-purpose programming language and there are very few computer systems in existence that are not set up for its use (i.e. where a C compiler does not exist).<br /><br />This C programming tutorial and course introduces you to the basics of programming in C. You will learn how programming languages work with data, what program flow is, and how to use functions, methods and routines. You will also get step-by-step instructions on how to create simple C programs and how to run them all while you learn C programming. <br />

This is a graduate course on the design and analysis of algorithms, covering several advanced topics not studied in typical introductory courses on algorithms. It is especially designed for doctoral students interested in theoretical computer science.

This course is offered to undergraduates and introduces students to the formulation, methodology, and techniques for numerical solution of engineering problems. Topics covered include: fundamental principles of digital computing and the implications for algorithm accuracy and stability, error propagation and stability, the solution of systems of linear equations, including direct and iterative techniques, roots of equations and systems of equations, numerical interpolation, differentiation and integration, fundamentals of finite-difference solutions to ordinary differential equations, and error and convergence analysis. The subject is taught the first half of the term.

This subject was originally offered in Course 13 (Department of Ocean Engineering) as 13.002J. In 2005, ocean engineering became part of Course 2 (Department of Mechanical Engineering), and this subject was renumbered 2.993J.

6.374 examines the device and circuit level optimization of digital building blocks. Topics covered include: MOS device models including Deep Sub-Micron effects; circuit design styles for logic, arithmetic and sequential blocks; estimation and minimization of energy consumption; interconnect models and parasitics; device sizing and logical effort; timing issues (clock skew and jitter) and active clock distribution techniques; memory architectures, circuits (sense amplifiers) and devices; testing of integrated circuits. The course employs extensive use of circuit layout and SPICE in design projects and software labs.

This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python programming language.

Course Format

This course has been designed for independent study. It provides everything you will need to understand the concepts covered in the course. The materials include:

• A complete set of Lecture Videos by Prof. Guttag.
• Resources for each lecture video, such as Handouts, Slides, and Code Files.
• Recitation Videos by course TA's to review content and problem solving techniques.
• Homework problems with sample student solutions.
• Further Study collections of links to supplemental online content.
• Self-Assessment tools, including lecture questions with answers and unit quizzes with solutions, to assess your subject mastery.