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What is “the mind” and what is artificial intelligence?
by David Quigley- 4.4
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Approx. 8 hours to complete
In this course, we will explore the history of cognitive science and the way these ideas shape how we think of artificial cognition. Introduction The Lure and Eeriness of Machine Life Introduction Tests and Thought Experiments The Turing Test Please excuse video interference "Computing Machinery and Intelligence" by A. M. Turing...
Advanced Data Structures in Java
by Leo Porter , Mia Minnes , Christine Alvarado- 4.8
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Approx. 29 hours to complete
How does Google Maps plan the best route for getting around town given current traffic conditions? How does an internet router forward packets of network traffic to minimize delay? How does an aid group allocate resources to its affiliated local partners? To solve such problems, we first represent the key pieces of data in a complex data structure....
Detección de objetos
by Antonio López Peña , Ernest Valveny , Maria Vanrell- 4.5
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Approx. 18 hours to complete
¿Te interesa la visión por computador? ¿Te gustaría conocer qué métodos puedes utilizar para detectar y reconocer objetos en una imagen? En este curso te introducirás en los principios básicos de cualquier sistema automático de detección y reconocimiento de objetos en imágenes. Finalizar el curso te permitirá: • Conocer las principales técnicas para la descripción y clasificación de una imagen,...
I/O-efficient algorithms
by Mark de Berg- 4.6
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Approx. 10 hours to complete
Operations on data become more expensive when the data item is located higher in the memory hierarchy. An operation on data in CPU registers is roughly a million times faster than an operation on a data item that is located in external memory that needs to be fetched first. Prerequisites: - O-notation, Ω-notation, Θ-notation; how to analyze algorithms...
Approximation Algorithms
by Mark de Berg- 4.7
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Approx. 15 hours to complete
Many real-world algorithmic problems cannot be solved efficiently using traditional algorithmic tools, for example because the problems are NP-hard. The goal of this course is to become familiar with important algorithmic concepts and techniques needed to effectively deal with such problems. We will see how to efficiently find such approximations. Prerequisites:...
Deep Learning in Computer Vision
by Anton Konushin , Alexey Artemov- 3.8
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Approx. 13 hours to complete
Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars....
Sesenta años de inteligencia artificial
by Carlos GershensonTop Instructor- 4.8
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Approx. 5 hours to complete
En este curso, ofrecido por la UNAM, cubriremos el pasado, presente y futuro de la inteligencia artificial. También mencionaremos los conceptos más importantes que serán útiles en el resto del programa especializado. Discutiremos las implicaciones sociales, éticas y filosóficas de los desarrollos en inteligencia artificial. El pensamiento de la inteligencia artificial...
Cloud Computing Concepts: Part 2
by Indranil Gupta- 4.6
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Approx. 20 hours to complete
Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies – all centered around distributed systems. Learn about such fundamental distributed computing "concepts" for cloud computing. Know how these systems work from the inside out. Get your hands dirty using these concepts with provided homework exercises....
算法设计与分析 Design and Analysis of Algorithms
by Wanling Qu- 4.7
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Approx. 25 hours to complete
课程教学目标 针对实际问题需求，进行数学建模并选择高效求解算法的训练，为提高学生的素质和创新能力打下必要的基础。主要内容涉及：面对实际问题建立数学模型、设计正确的求解算法、算法的效率估计、改进算法的途径、问题计算复杂度的估计、难解问题的确定和应对策略等等。本课程是算法课程的基础部分，主要涉及算法的设计、分析与改进途径，其他有关计算复杂性的内容将在后续课程中加以介绍。 课程内容安排 本课程的内容分成两大部分：算法的基础知识、通用算法设计技术与分析方法。 第一部分是算法基础知识，约占20%，主要介绍算法相关的基本概念和数学基础。比如，什么是算法的伪码描述？什么是算法最坏情况下和平均情况下的时间复杂度？算法时间复杂度函数的主要性质，算法复杂度估计中常用的数学方法，如序列求和及递推方程求解。 第二部分是通用的算法设计技术与分析方法，主要介绍分治策略、动态规划、贪心法、回溯与分支限界。主要介绍这些设计技术的使用条件、分析方法、改进途径，并给出一些重要的应用。 算法基础 001本周教学内容简介 002算法设计的两个例子 003问题的计算复杂度：排序问题 004货郎问题与计算复杂性 005算法及其时间复杂度 006算法的伪码表示 007函数的渐近的界 008有关函数渐近的界的定理 009几类重要的函数 第一周作业 序列求和与递推方程 010本周教学内容简介 011序列求和的方法 012递推方程与算法分析 013迭代法求解递推方程 014差消法求解递推方程 015递归树 016主定理及其证明 017主定理的应用 第二周作业 分治算法的设计与分析 018本周教学内容简介 019分治策略的设计思想 020分治算法的一般描述和分析方法 021芯片测试 022快速排序 023幂乘算法及应用 024改进分治算法的途径1：减少子问题数 025改进分治算法的途径2：增加预处理 第三周作业 分治算法的典型应用 026本周教学内容简介（01:19） 027选最大与选最小 028选第二大 029一般选择问题的算法设计 030一般选择问题的算法分析 031卷积及应用 032卷积计算 033快速傅立叶变换FFT算法 034平面点集的凸包 第四周作业 动态规划算法 035本周教学内容简介 036动态规划算法的例子...
Aprenda a ensinar programação com o Programaê!
by Tiago Maluta , Maristela Alcântara- 4.8
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Approx. 8 hours to complete
O curso "Aprenda a ensinar programação com o Programaê!" tem como objetivo apoiar professores no desenvolvimento de suas aulas com o ‘Programaê!’, um movimento que quer aproximar a programação do cotidiano de jovens de todo o Brasil por meio de um portal prático, agregador de ideias, soluções, dicas e planos de aula estruturados para professores....
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