Courses tagged with "Nutrition" (6413)

This course is a comprehensive introduction to control system synthesis in which the digital computer plays a major role, reinforced with hands-on laboratory experience. The course covers elements of real-time computer architecture; input-output interfaces and data converters; analysis and synthesis of sampled-data control systems using classical and modern (state-space) methods; analysis of trade-offs in control algorithms for computation speed and quantization effects. Laboratory projects emphasize practical digital servo interfacing and implementation problems with timing, noise, and nonlinear devices.

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 course develops the fundamentals of feedback control using linear transfer function system models. Topics covered include analysis in time and frequency domains; design in the s-plane (root locus) and in the frequency domain (loop shaping); describing functions for stability of certain non-linear systems; extension to state variable systems and multivariable control with observers; discrete and digital hybrid systems and use of z-plane design. Students will complete an extended design case study. Students taking the graduate version (2.140) will attend the recitation sessions and complete additional assignments.

Analysis I covers fundamentals of mathematical analysis: metric spaces, convergence of sequences and series, continuity, differentiability, Riemann integral, sequences and series of functions, uniformity, interchange of limit operations.

This course continues from Analysis I (18.100B), in the direction of manifolds and global analysis. The first half of the course covers multivariable calculus. The rest of the course covers the theory of differential forms in n-dimensional vector spaces and manifolds.

In this course we’ll explore complex analysis, complex dynamics, and some applications of these topics.

This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.

This class analyzes complex biological processes from the molecular, cellular, extracellular, and organ levels of hierarchy. Emphasis is placed on the basic biochemical and biophysical principles that govern these processes. Examples of processes to be studied include chemotaxis, the fixation of nitrogen into organic biological molecules, growth factor and hormone mediated signaling cascades, and signaling cascades leading to cell death in response to DNA damage. In each case, the availability of a resource, or the presence of a stimulus, results in some biochemical pathways being turned on while others are turned off. The course examines the dynamic aspects of these processes and details how biochemical mechanistic themes impinge on molecular/cellular/tissue/organ-level functions. Chemical and quantitative views of the interplay of multiple pathways as biological networks are emphasized. Student work culminates in the preparation of a unique grant application in an area of biological networks.

This course focuses on computational and experimental analysis of biological systems across a hierarchy of scales, including genetic, molecular, cellular, and cell population levels. The two central themes of the course are modeling of complex dynamic systems and protein design and engineering. Topics include gene sequence analysis, molecular modeling, metabolic and gene regulation networks, signal transduction pathways and cell populations in tissues. Emphasis is placed on experimental methods, quantitative analysis, and computational modeling.

The goal of this course is to investigate with students backgrounds on some of the pivotal events that have shaped our understanding and approach to architecture. Emphasis of discussion will be primarily on buildings and works of individual architects. Canonical architects, buildings and movements that have exerted significant influences on the development of architecture will be studied in detail. We will visit some of these buildings for a first-hand look and to evaluate for ourselves their significance or lack thereof. As a final project, each student will analyze a building through drawings, text, bibliography and a physical model in a format ready for documentation and exhibition.

An analysis of historical structures is presented themed sections based around construction materials. Structures from all periods of history are analyzed. The goal of the class is to provide an understanding of the preservation of historic structures for all students.

Analytic Combinatorics teaches a calculus that enables precise quantitative predictions of large combinatorial structures. This course introduces the symbolic method to derive functional relations among ordinary, exponential, and multivariate generating functions, and methods in complex analysis for deriving accurate asymptotics from the GF equations.

If chemistry is the science of stuff, then analytical chemistry answers the question: what is it? And how much of it do you have? This course teaches how to do this with instrumental analysis!

This is a laboratory course supplemented by lectures that focus on selected analytical facilities that are commonly used to determine the mineralogy, elemental abundance and isotopic ratios of Sr and Pb in rocks, soils, sediments and water.

Want to know how to avoid bad decisions with data?

Making good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.

These big picture ideas have motivated the development of quantitative models, but in most traditional statistics courses, these concepts get lost behind a wall of little techniques and computations. In this course we keep the focus on the ideas that really matter, and we illustrate them with lively, practical, accessible examples.

We will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?

This course will be is relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor’s degree contemplating graduate study in a business field.

Opportunities in analytics are abundant at the moment. Specific techniques or software packages may be helpful in landing first jobs, but those techniques and packages may soon be replaced by something newer and trendier. Understanding the ways in which quantitative models really work, however, is a management level skill that is unlikely to go out of style.

This course is part of the Business Principles and Entrepreneurial Thought XSeries.

Do you want to be more reflective in your teaching practice and wonder if there are technologies that can help? Are you curious about how data-driven, evidence-based teaching practices can improve your students’ learning? This is the course for you!

Analytics for the Classroom Teacher is an introduction to the emerging field of teaching and learning analytics from the perspective of a classroom teacher.

Experts from all over the world will provide an overview of the current state-of-the-art in teaching and learning analytics. You’ll learn how teachers, curriculum developers and policy makers are collecting and analysing data from the classroom to help guide decisions at all levels.

The course will then focus on the school teacher, and how data analytics can help you to make improvements in your classroom.

You’ll learn to use analytics to improve your lesson plans and your delivery of those plans, and discover more about your students' learning.

No previous knowledge in data-driven instruction, teaching and learning analytics is needed. Join us and a large community of innovative teachers from around the globe and become a pioneer of teaching and learning analytics in your school.

Data is the lifeblood of an organization. Competency in programming is an essential skill for successfully extracting information and knowledge from data.

The goal of this course is to introduce learners to the basics of programming in Python and to give a working knowledge of how to use programs to deal with data.

In this course, we will first cover the basics of programming and then focus on using Python on the entire data management process from data acquisition to analysis of data big data and small data.

This is an intensive hands-on course that will equip and reward learners with proficiency in data management skills.

This course covers the key quantitative methods of finance: financial econometrics and statistical inference for financial applications; dynamic optimization; Monte Carlo simulation; stochastic (Itô) calculus. These techniques, along with their computer implementation, are covered in depth. Application areas include portfolio management, risk management, derivatives, and proprietary trading.

All analytics work begins and ends with a story. Storytelling is the analytics professional’s missing link in delivering the essence of signals and insights from data to executives, management and more for real business results.

In this analytics storytelling course, you’ll learn effective strategies and tools to master data communication in the most impactful way possible—through well-crafted analytics stories.

This course focuses on alternative ways in which the issues of growth, restructuring, innovation, knowledge, learning, and accounting and measurements can be examined, covering both industrialized and emerging countries. We give special emphasis to recent transformations in regional economies throughout the world and to the implications these changes have for the theories and research methods used in spatial economic analyses. Readings will relate mainly to the United States, but we cover pertinent material on foreign countries in lectures.