Courses:
- Artificial Intelligence Planning
The University of Edinburgh.
The University of Edinburgh.
Overview
The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications. The January 2015 session was the final version of the course. It will remain open so that those interested can register and access all the materials.
The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications. The January 2015 session was the final version of the course. It will remain open so that those interested can register and access all the materials.
Details
The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications. It will allow you to:
The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications. It will allow you to:
• Understand different planning problems
• Have the basic know how to design and implement AI planning systems
• Know how to use AI planning technology for projects in different application domains
• Have the ability to make use of AI planning literature
• Have the basic know how to design and implement AI planning systems
• Know how to use AI planning technology for projects in different application domains
• Have the ability to make use of AI planning literature
Planning is a fundamental part of intelligent systems. In this course, for example, you will learn the basic algorithms that are used in robots to deliberate over a course of actions to take. Simpler, reactive robots don't need this, but if a robot is to act intelligently, this type of reasoning about actions is vital.
Course.
Course.
- Machine Learning
Stanford University .
Stanford University .
Overview.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
Details.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
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This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
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- Practical Machine Learning.
Johns Hopkins University.
Johns Hopkins University.
Overview
Learn the basic components of building and applying prediction functions with an emphasis on practical applications. This is the eighth course in the Johns Hopkins Data Science Specialization.
Learn the basic components of building and applying prediction functions with an emphasis on practical applications. This is the eighth course in the Johns Hopkins Data Science Specialization.
Details
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
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One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
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- Introduction to Recommender Systems.
University of Minnesota
University of Minnesota
Recommender systems have changed the way people find products, information, and even other people. They study patterns of behavior to know what someone will prefer from among a collection of things he has never experienced. The technology behind recommender systems has evolved over the past 20 years into a rich collection of tools that enable the practitioner or researcher to develop effective recommenders. We will study the most important of those tools, including how they work, how to use them, how to evaluate them, and their strengths and weaknesses in practice.
The algorithms we will study include content-based filtering, user-user collaborative filtering, item-item collaborative filtering, dimensionality reduction, and interactive critique-based recommenders. The approach will be hands-on, with six two week projects, each of which will involve implementation and evaluation of some type of recommender.
In addition to topical lectures, this course includes interviews and guest lectures with experts from both academia and industry.
Go to course (link)
Go to course (link)
- Text Mining and Analytics
University of Illinois at Urbana-Champaign
University of Illinois at Urbana-Champaign
Overview
Explore algorithms for mining and analyzing big text data to discover interesting patterns, extract useful knowledge, and support decision making.
Details
This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.
Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.
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Explore algorithms for mining and analyzing big text data to discover interesting patterns, extract useful knowledge, and support decision making.
Details
This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.
Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.
Go to course link
- Natural Language Processing
Columbia University
Columbia University
Overview
Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to key problems in natural language processing.
Details
Natural language processing (NLP) deals with the application of computational models to text or speech data. Application areas within NLP include automatic (machine) translation between languages; dialogue systems, which allow a human to interact with a machine using natural language; and information extraction, where the goal is to transform unstructured text into structured (database) representations that can be searched and browsed in flexible ways. NLP technologies are having a dramatic impact on the way people interact with computers, on the way people interact with each other through the use of language, and on the way people access the vast amount of linguistic data now in electronic form. From a scientific viewpoint, NLP involves fundamental questions of how to structure formal models (for example statistical models) of natural language phenomena, and of how to design algorithms that implement these models.
Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to key problems in natural language processing.
Details
Natural language processing (NLP) deals with the application of computational models to text or speech data. Application areas within NLP include automatic (machine) translation between languages; dialogue systems, which allow a human to interact with a machine using natural language; and information extraction, where the goal is to transform unstructured text into structured (database) representations that can be searched and browsed in flexible ways. NLP technologies are having a dramatic impact on the way people interact with computers, on the way people interact with each other through the use of language, and on the way people access the vast amount of linguistic data now in electronic form. From a scientific viewpoint, NLP involves fundamental questions of how to structure formal models (for example statistical models) of natural language phenomena, and of how to design algorithms that implement these models.
In this course you will study mathematical and computational models of language, and the application of these models to key problems in natural language processing. The course has a focus on machine learning methods, which are widely used in modern NLP systems: we will cover formalisms such as hidden Markov models, probabilistic context-free grammars, log-linear models, and statistical models for machine translation. The curriculum closely follows a course currently taught by Professor Collins at Columbia University, and previously taught at MIT.
Course link
Course link
- Neural Networks for Machine Learning
University of Toronto
University of Toronto
Overview
Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.
Details
Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains.
Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.
Details
Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains.
This YouTube video gives examples of the kind of material that will be in the course, but the course will present this material at a much gentler rate and with more examples.
Course link.
Course link.
- Detección de objetos
Universitat Autònoma de Barcelona
Universitat Autònoma de Barcelona
Overview
El curso ofrece la oportunidad de aprender las principales técnicas de visión por computador que permiten detectar y reconocer objetos en una imagen. Está orientado a estudiantes interesados en adquirir el conocimiento necesario para el desarrollo de aplicaciones reales de detección y reconocimiento de objetos.
Details
En este curso se introducen los principios básicos de cualquier sistema automático de detección y reconocimiento de objetos en imágenes. A lo largo del curso se analizan diferentes métodos de representación y clasificación que permiten abordar casos de aplicación de complejidad creciente.
El curso ofrece la oportunidad de aprender las principales técnicas de visión por computador que permiten detectar y reconocer objetos en una imagen. Está orientado a estudiantes interesados en adquirir el conocimiento necesario para el desarrollo de aplicaciones reales de detección y reconocimiento de objetos.
Details
En este curso se introducen los principios básicos de cualquier sistema automático de detección y reconocimiento de objetos en imágenes. A lo largo del curso se analizan diferentes métodos de representación y clasificación que permiten abordar casos de aplicación de complejidad creciente.
El curso está orientado tanto a estudiantes universitarios de algún grado relacionado con la informática, la ingenierÃa o las matemáticas, como a otros estudiantes con conocimientos de programación, interesados en aprender cómo utilizar técnicas de visión por computador para extraer información de las imágenes. El curso les ofrece los conocimientos y herramientas necesarios para que sean capaces de desarrollar sus propios sistemas de detección y reconocimiento de objetos en múltiples aplicaciones.
El contenido del curso se estructura a partir de un esquema básico de detección y reconocimiento de objetos que sirve de guÃa para ir introduciendo tanto los diferentes métodos de extracción de caracterÃsticas y representación de la imagen como diferentes alternativas para clasificar una imagen y para localizar todas las instancias de un objeto en la imagen. El temario del curso incluye conceptos básicos de formación de la imagen, la convolución y su aplicación a la detección de contornos, caracterÃsticas de regiones, descriptores de imagen (Local Binary Pattern, Histogram of Oriented Gradients, caracterÃsticas de Haar) y varios métodos de clasificación (clasificador lineal, Support Vector Machine, Adaboost, Random Forest, Convolutional Neural Network).
Los objetivos del curso son:
• diseñar, a partir de un esquema básico común, soluciones adaptadas para diferentes problemas de detección y reconocimiento de objetos en una imagen,
• conocer las principales técnicas para la descripción y clasificación de una imagen,
• conocer las herramientas que permiten el desarrollo de aplicaciones reales de detección y reconocimiento de objetos.
Course link
• conocer las principales técnicas para la descripción y clasificación de una imagen,
• conocer las herramientas que permiten el desarrollo de aplicaciones reales de detección y reconocimiento de objetos.
Course link
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