Courses tagged with "Nutrition" (421)

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.

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.

This course is the second of a two-term sequence with 6.450. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise (AWGN) channels, their performance analysis, and design principles. After a review of 6.450 and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-decision decoding. It continues with binary linear block codes, Reed-Muller codes, finite fields, Reed-Solomon and BCH codes, binary linear convolutional codes, and the Viterbi algorithm.

More advanced topics include trellis representations of binary linear block codes and trellis-based decoding; codes on graphs; the sum-product and min-sum algorithms; the BCJR algorithm; turbo codes, LDPC codes and RA codes; and performance of LDPC codes with iterative decoding. Finally, the course addresses coding for the bandwidth-limited regime, including lattice codes, trellis-coded modulation, multilevel coding and shaping. If time permits, it covers equalization of linear Gaussian channels.

This course studies basic optimization and the principles of optimal control. It considers deterministic and stochastic problems for both discrete and continuous systems. The course covers solution methods including numerical search algorithms, model predictive control, dynamic programming, variational calculus, and approaches based on Pontryagin's maximum principle, and it includes many examples and applications of the theory.

Learn how to write composable software that stays responsive at all times by being elastic under load and resilient in the presence of failures. Model systems after human organizations or inter-human communication.

This course is an introduction to the design, analysis, and fundamental limits of wireless transmission systems. Topics to be covered include: wireless channel and system models; fading and diversity; resource management and power control; multiple-antenna and MIMO systems; space-time codes and decoding algorithms; multiple-access techniques and multiuser detection; broadcast codes and precoding; cellular and ad-hoc network topologies; OFDM and ultrawideband systems; and architectural issues.

In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.

Welcome to 6.041/6.431, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. For example:

• The concept of statistical significance (to be touched upon at the end of this course) is considered by the Financial Times as one of "The Ten Things Everyone Should Know About Science".
• A recent Scientific American article argues that statistical literacy is crucial in making health-related decisions.
• Finally, an article in the New York Times identifies statistical data analysis as an upcoming profession, valuable everywhere, from Google and Netflix to the Office of Management and Budget.

The aim of this class is to introduce the relevant models, skills, and tools, by combining mathematics with conceptual understanding and intuition.

Basic concepts of computer programming are introduced, starting with the notion of an algorithm. Emphasis is on developing the ability to write programs to solve practical computational problems.

Topics include:

• Algorithms
• Elements of C/C++ programming languages
• Basic data types
• Sequential and conditional execution
• Iterative solutions
• Arrays, matrices and their applications
• Functions
• Sorting and searching
• Elements of string processing
• Introduction to pointers
• Basics of Software Engineering
• Structures
• File Processing

Learners will read and understand many sample programs, and will have to write several on their own. This course deals with basic programming, and sets the foundation for solid programming practices for beginners.

This course is part of the Fundamentals of Computer Science XSeries Program: 

• Object-Oriented Programming
• Foundations of Data Structures
• Implementation of Data Structures
• Algorithms

In this course----the third in a trans-institution sequence of MOOCs on Mobile Cloud Computing with Android--we will learn how to connect Android mobile devices to cloud computing and data storage resources, essentially turning a device into an extension of powerful cloud-based services on popular cloud computing platforms, such as Google App Engine and Amazon EC2.