Online courses directory (548)

6.637 covers the fundamentals of optical signals and modern optical devices and systems from a practical point of view. Its goal is to help students develop a thorough understanding of the underlying physical principles such that device and system design and performance can be predicted, analyzed, and understood.

Most optical systems involve the use of one or more of the following: sources (e.g., lasers and light-emitting diodes), light modulation components (e.g., liquid-crystal light modulators), transmission media (e.g., free space or fibers), photodetectors (e.g., photodiodes, photomultiplier tubes), information storage devices (e.g., optical disk), processing systems (e.g., imaging and spatial filtering systems) and displays (LCOS microdisplays). These are the topics covered by this course.

This course introduces the principal algorithms for linear, network, discrete, nonlinear, dynamic optimization and optimal control. Emphasis is on methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton's method, heuristic methods, and dynamic programming and optimal control methods.

This course introduces students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, transportation, marketing, project management, and finance. Includes a team project in which students select and solve a problem in practice.

With existing data, you will develop skills in data analysis and basic statistics by exploring your own research question.

In this course--the second in a trans-institution sequence of MOOCs on Mobile Cloud Computing with Android--we will learn how to apply patterns, pattern languages, and frameworks to alleviate the complexity of developing concurrent and networked services on mobile devices running Android that connect to popular cloud computing platforms.

Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard practice.

This class is a hands-on, project-based introduction to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, cache and memory hierarchy optimization, parallel programming, and building scalable distributed systems.

The course also includes design reviews with industry mentors, as described in this MIT News article.

This course is broad, covering a wide range of topics that have to do with the post-pc era of computing. It is a hands-on project course that also includes some foundational subjects. Students will program iPAQ handheld computers, cell phones (series 60 phones), speech processing, vision, Cricket location systems, GPS, and more. Most of the programming will be using Python®, but Python® can be learned and mastered during the course.

This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5508 (Pervasive Computing).

Data Analysis for Genomics will teach students how to harness the wealth of genomics data arising from new technologies, such as microarrays and next generation sequencing, in order to answer biological questions, both for basic cell biology and clinical applications.

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 offered to graduates and focuses on understanding the fundamental principles of the "front-end" processes used in the fabrication of devices for silicon integrated circuits. This includes advanced physical models and practical aspects of major processes, such as oxidation, diffusion, ion implantation, and epitaxy. Other topics covered include: high performance MOS and bipolar devices including ultra-thin gate oxides, implant-damage enhanced diffusion, advanced metrology, and new materials such as Silicon Germanium (SiGe).

6.334 examines the application of electronics to energy conversion and control. Topics covered include: modeling, analysis, and control techniques; design of power circuits including inverters, rectifiers, and DC-DC converters; analysis and design of magnetic components and filters; and characteristics of power semiconductor devices. Numerous application examples will be presented such as motion control systems, power supplies, and radio-frequency power amplifiers. The course is worth 6 engineering design points.

Learn the basic components of building and applying prediction functions with an emphasis on practical applications. This is the eighth course in the Johns Hopkins Data Science Specialization.

This course provides a thorough introduction to the C programming language, the workhorse of the UNIX operating system and lingua franca of embedded processors and micro-controllers. The first two weeks will cover basic syntax and grammar, and expose students to practical programming techniques. The remaining lectures will focus on more advanced concepts, such as dynamic memory allocation, concurrency and synchronization, UNIX signals and process control, library development and usage. Daily programming assignments and weekly laboratory exercises are required. Knowledge of C is highly marketable for summer internships, UROPs, and full-time positions in software and embedded systems development.

This class is intended for students who have a basic understanding of spreadsheets and are now ready to delve deeper into formatting, formulas and functions, multi-page spreadsheets, charting data, creating tables that have database features, and be introduced to pivot tables. This class was designed to be an active, hands-on class. You will be creating Excel® spreadsheets and have files to open and follow along as you progress through the units. This class is not intended for a specific occupation or activity, but when you are finished with this class, you will be able to use Excel® in a variety of circumstances to format and manipulate numerical data. Although the resources in this course use Microsoft Excel® 2010, it should be noted that all of the skills and tasks that you will be asked to complete can be done in any version of Excel®. If you stay flexible enough in your thinking and search out the commands and icons on whatever software you are using, you will succeed.

Principles of Applied Mathematics is a study of illustrative topics in discrete applied mathematics including sorting algorithms, information theory, coding theory, secret codes, generating functions, linear programming, game theory. There is an emphasis on topics that have direct application in the real world.

This course was recently revised to meet the MIT Undergraduate Communication Requirement (CR). It covers the same content as 18.310, but assignments are structured with an additional focus on writing.

This course surveys a variety of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research.

Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.

6.826 provides an introduction to the basic principles of computer systems, with emphasis on the use of rigorous techniques as an aid to understanding and building modern computing systems. Particular attention is paid to concurrent and distributed systems. Topics covered include: specification and verification, concurrent algorithms, synchronization, naming, networking, replication techniques (including distributed cache management), and principles and algorithms for achieving reliability.

This course introduces the basic mathematical and programming principles that underlie much of Computer Science. Students will refine their programming skills as well as learn the basics of creating efficient solutions to common computational problems.

This course provides an introduction to Python and elementary principles of computing, including iteration, recursion, and binary representation of data. Additional topics on cellular automata, encryption, and the limits of computation are also introduced. The goal of this course is to introduce some of the techniques used in computer science to solve complex problems, with or without a computer. The Python modules are used to introduce and illustrate conditionals and iteration; additional topics are provided without using a specific programming language and can be incorporated into classes that do not use Python.

The course serves as an introduction to the theory and practice behind many of today's communications systems. 6.450 forms the first of a two-course sequence on digital communication. The second class, 6.451 Principles of Digital Communication II, is offered in the spring.

Topics covered include: digital communications at the block diagram level, data compression, Lempel-Ziv algorithm, scalar and vector quantization, sampling and aliasing, the Nyquist criterion, PAM and QAM modulation, signal constellations, finite-energy waveform spaces, detection, and modeling and system design for wireless communication.