Courses tagged with "Nutrition" (421)

This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.

6.777J / 2.372J is an introduction to microsystem design. Topics covered include: material properties, microfabrication technologies, structural behavior, sensing methods, fluid flow, microscale transport, noise, and amplifiers feedback systems. Student teams design microsystems (sensors, actuators, and sensing/control systems) of a variety of types, (e.g., optical MEMS, bioMEMS, inertial sensors) to meet a set of performance specifications (e.g., sensitivity, signal-to-noise) using a realistic microfabrication process. There is an emphasis on modeling and simulation in the design process. Prior fabrication experience is desirable. The course is worth 4 Engineering Design Points.

Understanding how to approach programming problems and devise a solution is an essential skill for any Python developer. In this course, you’ll learn new concepts, patterns, and methods that will expand your coding abilities from programming expert, Peter Norvig.

This course provides you with opportunities to integrate and apply your information security knowledge.

Learn the basics of creating data products using Shiny, R packages, and interactive graphics. This is the ninth course in the Johns Hopkins Data Science Specialization.

This course examines the role of the engineer as patent expert and as technical witness in court and patent interference and related proceedings. It discusses the rights and obligations of engineers in connection with educational institutions, government, and large and small businesses. It compares various manners of transplanting inventions into business operations, including development of New England and other U.S. electronics and biotechnology industries and their different types of institutions. The course also considers American systems of incentive to creativity apart from the patent laws in the atomic energy and space fields.

Acknowledgment

The instructors would like to thank Joanne Rines and Elijah Ercolino for their efforts in preparing this course.

In this course, you'll hone your problem-solving skills through learning to find numerical solutions to systems of differential equations. You'll write code in Python to fight forest fires, rescue the Apollo 13 astronauts, stop the spread of epidemics, and resolve other real-world dilemmas.

Explore the latest trends in digital marketing. Get beneath the hype. What are the implications? How will you respond?

El curso es bilingüe español-inglés, con videos y materiales en ambos idiomas. Lectures and materials are provided both in Spanish and English.

We will present the state of the art energy minimization algorithms that are used to perform inference in modern artificial vision models: that is, efficient methods for obtaining the most likely interpretation of a given visual input. We will also cover the popular max-margin framework for estimating the model parameters using inference.

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.

Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals. This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyze, and understand insightful models for a broad range of these processes. The range of areas for which discrete stochastic-process models are useful is constantly expanding, and includes many applications in engineering, physics, biology, operations research and finance.

This class addresses the representation, analysis, and design of discrete time signals and systems. The major concepts covered include: Discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flowgraph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; multirate techniques; Hilbert transforms; Cepstral analysis and various applications.

Acknowledgements

I would like to express my thanks to Thomas Baran, Myung Jin Choi, and Xiaomeng Shi for compiling the lecture notes on this site from my individual lectures and handouts and their class notes during the semesters that they were students in the course. These lecture notes, the text book and included problem sets and solutions will hopefully be helpful as you learn and explore the topic of Discrete-Time Signal Processing.

Distributed algorithms are algorithms designed to run on multiple processors, without tight centralized control. In general, they are harder to design and harder to understand than single-processor sequential algorithms. Distributed algorithms are used in many practical systems, ranging from large computer networks to multiprocessor shared-memory systems. They also have a rich theory, which forms the subject matter for this course.

The core of the material will consist of basic distributed algorithms and impossibility results, as covered in Prof. Lynch's book Distributed Algorithms. This will be supplemented by some updated material on topics such as self-stabilization, wait-free computability, and failure detectors, and some new material on scalable shared-memory concurrent programming.

This course covers abstractions and implementation techniques for the design of distributed systems. Topics include: server design, network programming, naming, storage systems, security, and fault tolerance. The assigned readings for the course are from current literature. This course is worth 6 Engineering Design Points.

The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.

The course addresses dynamic systems, i.e., systems that evolve with time. Typically these systems have inputs and outputs; it is of interest to understand how the input affects the output (or, vice-versa, what inputs should be given to generate a desired output). In particular, we will concentrate on systems that can be modeled by Ordinary Differential Equations (ODEs), and that satisfy certain linearity and time-invariance conditions.

We will analyze the response of these systems to inputs and initial conditions. It is of particular interest to analyze systems obtained as interconnections (e.g., feedback) of two or more other systems. We will learn how to design (control) systems that ensure desirable properties (e.g., stability, performance) of the interconnection with a given dynamic system.

This course provides an introduction to nonlinear deterministic dynamical systems. Topics covered include: nonlinear ordinary differential equations; planar autonomous systems; fundamental theory: Picard iteration, contraction mapping theorem, and Bellman-Gronwall lemma; stability of equilibria by Lyapunov's first and second methods; feedback linearization; and application to nonlinear circuits and control systems.

Dieser Kurs vermittelt einen Überblick über die Grundlagen des Maschinellen Sehens an Hand der Extraktion von 3D-Information aus dem Stereokamerabild einer Szene.

This course teaches the principles and analysis of electromechanical systems. Students will develop analytical techniques for predicting device and system interaction characteristics as well as learn to design major classes of electric machines. Problems used in the course are intended to strengthen understanding of the phenomena and interactions in electromechanics, and include examples from current research.