Courses tagged with "Nutrition" (6413)

This half-semester Samplings course, worth six instead of the typical twelve credits, drew attention to the thirteen female Nobel laureates. As the MIT Literature website explains, Samplings serve students looking for "a less intensive, more discussion and reading oriented way of continuing literary study." Secondly, "they allow the Literature Faculty to offer occasional subjects that cannot be permanently and regularly offered. Finally, they are a site of experimentation—a way of trying out new authors and new themes."

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.

This course introduces students to the modeling, quantification, and analysis of uncertainty.  The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data. These tools underlie important advances in many fields, from the basic sciences to engineering and management.

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:

• Lecture Videos by MIT Professor John Tsitsiklis
• Lecture Slides and Readings
• Recitation Problems and Solutions
• Recitation Help Videos by MIT Teaching Assistants
• Tutorial Problems and Solutions
• Tutorial Help Videos by MIT Teaching Assistants
• Problem Sets with Solutions
• Exams with Solutions

How should we interpret chance around us? Watch beautiful mathematical ideas emerge in a glorious historical tapestry as we discover key concepts in probability, perhaps as they might first have been unearthed, and illustrate their sway with vibrant applications taken from history and the world around.

Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning. Expressions of degree of belief were used in language long before people began codifying the laws of probability theory. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning.

This class is suitable for advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.

This course covers interpretations of the concept of probability. Topics include basic probability rules; random variables and distribution functions; functions of random variables; and applications to quality control and the reliability assessment of mechanical/electrical components, as well as simple structures and redundant systems. The course also considers elements of statistics; Bayesian methods in engineering; methods for reliability and risk assessment of complex systems (event-tree and fault-tree analysis, common-cause failures, human reliability models); uncertainty propagation in complex systems (Monte Carlo methods, Latin Hypercube Sampling); and an introduction to Markov models. Examples and applications are drawn from nuclear and other industries, waste repositories, and mechanical systems.

This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.

This class covers quantitative analysis of uncertainty and risk for engineering applications. Fundamentals of probability, random processes, statistics, and decision analysis are covered, along with random variables and vectors, uncertainty propagation, conditional distributions, and second-moment analysis. System reliability is introduced. Other topics covered include Bayesian analysis and risk-based decision, estimation of distribution parameters, hypothesis testing, simple and multiple linear regressions, and Poisson and Markov processes. There is an emphasis placed on real-world applications to engineering problems.

Our capacity to collect and store data has exponentially increased, but deriving information from data from a scientific perspective requires a foundational knowledge of probability.

Are you interested in a career in the emerging data science field, or as an actuarial scientist? Or want better to understand statistical theory and mathematical modeling?

In this statistics and data analysis course, we will provide an introduction to mathematical probability to help meet your career goals in the exciting new areas becoming known as information science.

In this course, we will first introduce basic probability concepts and rules, including Bayes theorem, probability mass functions and CDFs, joint distributions and expected values.

Then we will discuss a few important probability distribution models with discrete random variables, including Bernoulli and Binomial distributions, Geometric distribution, Negative Binomial distribution, Poisson distribution, Hypergeometric distribution and discrete uniform distribution.

To continue learning about probability, enroll in Probability: Distribution Models & Continuous Random Variables, which covers continuous distribution models, central limit theorem and more.

 
The Center for Science of Information, a National Science Foundation Center, supports learners by offering free educational resources in information science.

In this statistics and data analysis course, you will learn about continuous random variables and some of the most frequently used probability distribution models including, exponential distribution, Gamma distribution, Beta distribution, and most importantly, normal distribution.

You will learn how these distributions can be connected with the Normal distribution by Central limit theorem (CLT). We will discuss Markov and Chebyshev inequalities, order statistics, moment generating functions and transformation of random variables.

This course along with the recommended pre-requisite, Probability: Basic Concepts & Discrete Random Variables, will you give the skills and knowledge to progress towards an exciting career in information and data science.

 
The Center for Science of Information, a National Science Foundation Center, supports learners by offering free educational resources in information science.

What do you do when you encounter a problem? This self-paced career development course will help you think critically and creatively to solve problems and design solutions. These skills will set you apart from peers and make you a sought-after leader and teammate.

This is the third course in Fullbridge’s four-part Career Development XSeries, designed to prepare you to succeed in the modern workplace.

This course, which is geared toward Freshmen, is an undergraduate seminar on mathematical problem solving. It is intended for students who enjoy solving challenging mathematical problems and who are interested in learning various techniques and background information useful for problem solving. Students in this course are expected to compete in a nationwide mathematics contest for undergraduates.

The Problem Solving with Analytics course provides students with the foundational knowledge to use data analytics to create business insights. You will learn:

The course has two goals. First, to give you a sense of what philosophers think about and why. Here we look at a number of perennial philosophical problems, including some or all of: how knowledge differs from "mere opinion," the objectivity (or not) of moral judgment, logical paradoxes, mind/body relations, the nature and possibility of free will, and how a person remains the same over time, as their bodily and psychological traits change. The second goal is to get you thinking philosophically yourself. This will help you develop your critical and argumentative skills more generally. Readings will be from late, great classical authors and influential contemporary figures.

This course introduces dynamic processes and the engineering tasks of process operations and control. Subject covers modeling the static and dynamic behavior of processes; control strategies; design of feedback, feedforward, and other control structures; and applications to process equipment.

Dedication

In preparing this material, the author has recalled with pleasure his own introduction, many years ago, to Process Control. This OCW course is dedicated with gratitude, to Prof. W. C. Clements of the University of Alabama.

The Improving Business Finances and Operations Specialization will be discontinued in January 2016. Learners who wish to earn a Specialization Certificate must pass all courses by December 31, 2015, and complete the Capstone Project in January 2016. If you do not anticipate completing the Specialization within this time frame, please do not purchase the full access option. Gain practical skills for analyzing and improving work processes and for organizing initiatives for continuous improvement of processes. Learn how to plan and execute process improvements with frameworks and techniques from Six Sigma and Lean Management initiatives.

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

Nesse curso, você irá entender um ingrediente fundamental da revolução digital: a amostragem, que permite que sinais como músicas e imagens sejam armazenados e processados em dispositivos digitais.

Want to store and process data at scale? This data analysis course teaches you how to apply the power of the Azure cloud to big data using Azure Data Lake technologies.

Learn how to manage data in Azure Data Lake Store and run U-SQL jobs in Azure Data Lake Analytics to generate insights from structured and unstructured data sources.

Note: To complete this course, you will need a Microsoft Azure subscription. You can sign up for a free trial subscription at http://azure.microsoft.com, or you can use your existing subscription. The labs have been designed to minimize the resource costs required to complete the hands-on activities.