Online courses directory (548)
6.101 is an introductory experimental laboratory that explores the design, construction, and debugging of analog electronic circuits. Lectures and six laboratory projects investigate the performance characteristics of diodes, transistors, JFETs, and op-amps, including the construction of a small audio amplifier and preamplifier. Seven weeks are devoted to the design and implementation, and written and oral presentation of a project in an environment similar to that of engineering design teams in industry. The course provides opportunity to simulate real-world problems and solutions that involve trade offs and the use of engineering judgment. Engineers from local analog engineering companies come to campus to help students with their design projects.
This course teaches the design of contemporary information systems for biological and medical data. Examples are chosen from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (e.g. C, C++, Java, Lisp, Perl, Python). A major term project is required of all students. This subject is open to motivated seniors having a strong interest in biomedical engineering and information system design with the ability to carry out a significant independent project.
This course was offered as part of the Singapore-MIT Alliance (SMA) program as course number SMA 5304.
This course offers an advanced introduction to numerical linear algebra. Topics include direct and iterative methods for linear systems, eigenvalue decompositions and QR/SVD factorizations, stability and accuracy of numerical algorithms, the IEEE floating point standard, sparse and structured matrices, preconditioning, linear algebra software. Problem sets require some knowledge of MATLAB®.
This course will focus on fundamental subjects in convexity, duality, and convex optimization algorithms. The aim is to develop the core analytical and algorithmic issues of continuous optimization, duality, and saddle point theory using a handful of unifying principles that can be easily visualized and readily understood.
The World Wide Web (WWW) has become the primary means by which we conduct searches and perform billing transactionsevents that can only occur with the support of specific applications. The purpose of this course is to introduce you to the design and development of such applications. This course will expose you to the basic fundamentals of the Internet and Web protocols, the different architectures that Web-related applications use, and the programming languages that enable the development of Web applications, placing particular emphasis on JavaScript, HTML, XML, AJAX, and Java Server Pages (JSP). We will also cover matters of security and reliability in the development of web applications via the use of transport encryption and authentication.
This course will introduce you to modern operating systems. We will focus on UNIX-based operating systems, though we will also learn about alternative operating systems, including Windows. The course will begin with an overview of the structure of modern operating systems. Over the course of the subsequent units, we will discuss the history of modern computers, analyze in detail each of the major components of an operating system (from processes to threads), and explore more advanced topics in the field, including memory management and file input/output. The class will conclude with a discussion of various system-related security issues.
This course examines the fundamentals of detection and estimation for signal processing, communications, and control. Topics covered include: vector spaces of random variables; Bayesian and Neyman-Pearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimum-variance unbiased estimators and the Cramer-Rao bounds; representations for stochastic processes, shaping and whitening filters, and Karhunen-Loeve expansions; and detection and estimation from waveform observations. Advanced topics include: linear prediction and spectral estimation, and Wiener and Kalman filters.
6.171 is a course for students who already have some programming and software engineering experience. The goal is to give students some experience in dealing with those challenges that are unique to Internet applications, such as:
- concurrency;
- unpredictable load;
- security risks;
- opportunity for wide-area distributed computing;
- creating a reliable and stateful user experience on top of unreliable connections and stateless protocols;
- extreme requirements and absurd development schedules;
- requirements that change mid-way through a project, sometimes because of experience gained from testing with users;
- user demands for a multi-modal interface.
