Courses tagged with "Nutrition" (421)

6.896 covers mathematical foundations of parallel hardware, from computer arithmetic to physical design, focusing on algorithmic underpinnings. Topics covered include: arithmetic circuits, parallel prefix, systolic arrays, retiming, clocking methodologies, boolean logic, sorting networks, interconnection networks, hypercubic networks, P-completeness, VLSI layout theory, reconfigurable wiring, fat-trees, and area-time complexity.

This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5511 (Theory of Parallel Hardware).

In this graduate-level course, we will be covering advanced topics in combinatorial optimization. We will start with non-bipartite matchings and cover many results extending the fundamental results of matchings, flows and matroids. The emphasis is on the derivation of purely combinatorial results, including min-max relations, and not so much on the corresponding algorithmic questions of how to find such objects. The intended audience consists of Ph.D. students interested in optimization, combinatorics, or combinatorial algorithms.

Wavelets are localized basis functions, good for representing short-time events. The coefficients at each scale are filtered and subsampled to give coefficients at the next scale. This is Mallat's pyramid algorithm for multiresolution, connecting wavelets to filter banks. Wavelets and multiscale algorithms for compression and signal/image processing are developed. Subject is project-based for engineering and scientific applications.

Learn how to program all the major systems of a robotic car from the leader of Google and Stanford's autonomous driving teams. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars. This course is offered as part of the Georgia Tech Masters in Computer Science. The updated course includes a final project, where you must chase a runaway robot that is trying to escape!

Learn about the inner workings of cryptographic primitives and how to apply this knowledge in real-world applications!

Learn about the inner workings of cryptographic primitives and protocols and how to apply this knowledge in real-world applications.

Tired of solving Sudokus by hand? This class teaches you how to solve complex search problems with discrete optimization concepts and algorithms, including constraint programming, local search, and mixed-integer programming.

The course covers the basics: representing games and strategies, the extensive form (which computer scientists call game trees), repeated and stochastic games, coalitional games, and Bayesian games (modeling things like auctions).

Learn about General Game Playing (GGP) and develop GGP programs capable of competing against humans and other programs in GGP competitions .

This course introduces concepts, languages, techniques, and patterns for programming heterogeneous, massively parallel processors. Its contents and structure have been significantly revised based on the experience gained from its initial offering in 2012. It covers heterogeneous computing architectures, data-parallel programming models, techniques for memory bandwidth management, and parallel algorithm patterns.

Communicating With Data has a distinctive structure and content, combining fundamental quantitative techniques of using data to make informed management decisions with illustrations of how real decision makers, even highly trained professionals, fall prey to errors and biases in their understanding. We present the fundamental concepts underlying the quantitative techniques as a way of thinking, not just a way of calculating, in order to enhance decision-making skills. Rather than survey all of the techniques of management science, we stress those fundamental concepts and tools that we believe are most important for the practical analysis of management decisions, presenting the material as much as possible in the context of realistic business situations from a variety of settings. Exercises and examples drawn from marketing, finance, operations management, strategy, and other management functions.

When developing chips it is essential that they get verified thoroughly because it is very hard or impossible to fix them once they have been manufactured. In this class, you will learn how to program verification environments that verify chip functionality efficiently, as well as understand and leverage automation such as constrained random test generation and improve code reuse leveraging a standardized methodology.

In an introduction to the basics of the famous Customer Development Process, Steve Blank provides insight into the key steps needed to build a successful startup. The main idea in this course is learning how to rapidly develop and test ideas by gathering massive amounts of customer and marketplace feedback. Many startups fail by not validating their ideas early on with real-life customers. In order to mitigate that, students will learn how to get out of the building and search for the real pain points and unmet needs of customers. Only with these can the entrepreneur find a proper solution and establish a suitable business model. Building a startup is not simply building an execution plan for a business model that the entrepreneur thinks will work, but rather, a search for the actual business model itself.

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination. ***Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!***

Try to picture yourself sitting down with your computer, ready to start developing a fully functional web application for the first time, available online for millions to use. “Where should I even begin? How long is this going to take me? Am I making any mistakes along the way?” The questions may leave you with an uneasy feeling that you will learn many lessons the hard way. In this intermediate course, Steve Huffman will teach you everything he wished he knew when he started building Reddit and, more recently, Hipmunk, as a lead engineer. Starting from the basics of how the web works, this course will walk you through core web development concepts such as how internet and browsers fit together, form validations, databases, APIs, integrating with other websites, scaling issues, and more; all of which form part of the knowledge it takes to build a web application of your own.

Helping you build human-centered design skills, so that you have the principles and methods to create excellent interfaces with any technology.

Learn to defend and protect vital company information using the latest technology and defense strategies. Analyze internal and external threats to proactively prevent information attacks. Gain experience by solving real-world problems and leave the class equipped to establish and oversee information security.

Ce cours introduit la programmation orientée objet en l'illustrant en langage Java. Il présuppose connues les bases de la programmation (variables, types, boucles, fonctions, ...). Il est conçu comme la suite du cours «Initiation à la programmation (en Java)».

What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common?

They are all complex real world problems being solved with applications of intelligence (AI).

This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.

You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.

Hands on experience will be gained by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to machine learning includes work on linear regression.

Want to learn how your radio works? Wondering how to implement filters using resistors, inductors, and capacitors? Wondering what are some other applications of RLC and CMOS circuits? This free circuit course, taught by edX CEO and MIT Professor Anant Agarwal and MIT colleagues, is for you.

The third and final online Circuits and Electronics courses is taken by all MITElectrical Engineering and Computer Science (EECS) majors.

Topics covered include: dynamics of capacitor, inductor and resistor networks; design in the time and frequency domains; op-amps, and analog and digital circuits and applications. Design and lab exercises are also significant components of the course.

Weekly coursework includes interactive video sequences, readings from the textbook, homework, online laboratories, and optional tutorials. The course will also have a final exam.

This is a self-paced course, so there are no weekly deadlines. However, all assignments are due by June 15, 2019, when the course will close.

Student Testimonials

“Brilliant course! It's definitely the best introduction to electronics in Universe! Interesting material, clean explanations, well prepared quizzes, challenging homeworks and fun labs.” - Ilya.

“6.002x will be a classic in the field of online learning. It combines Prof. Agarwal's enthusiasm for electronics and education. The online circuit design program works very well. The material is difficult. I took the knowledge from the class and built an electronic cat feeder.” - Stan