A national initiative on education and competence in Artificial Intelligence

Machine Learning

Machine learning (ML) is a field of artificial intelligence that uses data-driven techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.











Umeå

Artificial intelligence (AI) is a term used for describing technologies that are used for designing and constructing intelligent computer programs. There is a vast amount of AI algorithms and AI software tools to build intelligent computer programs. This course is an introduction to AI for professionals in industry and public organisations who have knowledge in engineering and system development. The course gives a wide perspective on different well-established AI methods and tools in order to show the potential opportunities to develop new AI-based technologies. The main themes of the course are theories and algorithms, which go from classical AI to emerging machine learning algorithms. The course also introduces social implications of the AI-based technologies, such as responsible development of AI.

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Örebro

The course offers knowledge on Robot Operating System (ROS), how it works and how it is applied within a variety of artificial intelligence and robotics areas. You will use ROS to design parts of an intelligent system and test these both in simulation as well as on existing robot platforms.

Read more at ÖREBRO UNIVERSITY

Online

This on-line course will teach you how to build convolutional neural networks. You will learn to design intelligent systems using deep learning for classification, annotation, and object recognition. This course is possible to combine with a full-time employment.

Read more at Mälardalen University

Online

I denna kurs behandlas designmetoder och olika algoritmer för och varianter av Deep Learning inom klassifiering, prediktion, interaktion och modellering med användning av olika typer av data; ljud, bild, video, text, sekvenser. Begrepp såsom Överträning, Dropouts och Gradienter gås igenom.   Installation och användning av hård- och mjukvaror för experimenterande med topologier såsom CNN, RNN, LSTM, DQN och tillhörande parametrar samt inspektion av resultatet utgör en väsentlig del av kursen. Kursen går även kortfattat igenom Dataanalys, Maskininlärning, Tensorer, Datorseende, Transfer Learning,  Grunder i Robotik, Reinforcement Learning och metoder med kombinationen Deep Reinforcement Learning. Numeriska implementationer studeras översiktligt. Det är en tidsmässig fördel att ha tillgång till lämpliga beräknings(grafik)kort alternativt ha/få tillgång till motsvarande molntjänst. Man kan köra Windows, OS X eller Linux.

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Göteborg

The course gives a survey of theory and practical computational implementations of how Natural Language interacts with the physical world. We will look at topics such as semantic theories of human language, action and perception, situated dialogue, situated language acquisition, grounding of language in action and perception, spatial cognition, generation and interpretation of scene descriptions from images and videos, integrated robotic dialogue systems, etc.

Read more at UNIVERSITY OF GOTHENBURG

Online

The on-line course will give insights in fundamental concepts of machine learning and actionable forecasting using predictive analytics. It will cover the key concepts to extract useful information and knowledge from big data sets for analytical modelling. This course is possible to combine with a full-time employment.

Read more at Mälardalen University

Göteborg

Artificiell intelligens (AI) studerar hur datorer kan utföra uppgifter som traditionellt har ansetts kräva mänsklig intelligens. Kursen ger en introduktion till ämnet och har två huvudsyften. Det ena syftet är att ge en förståelse för vilka delområden som finns inom AI, deras historiska utveckling, och vilka etiska problemställningar som kan uppkomma inom olika delområden. Detta görs genom att läsa litteratur inom olika AI-områden, att sammanfatta och diskutera litteraturen skriftligt, och att granska uppsatser av andra studenter. Det andra syftet är att lära ut grundläggande begrepp och algoritmer för heuristisk (informerad) sökning, planering och problemlösning, inklusive deras användningsområden, samt hur de kan användas för att lösa intressanta AI-problem.  

Read more at UNIVERSITY OF GOTHENBURG

Göteborg

Kursen ger en introduktion till maskininlärning med hjälp av artificiella neurala nätverk (ANN). Fokus är på praktiska övningar och konkreta exempel för att ge både en översiktlig förståelse för ANN och färdigheter i att använda etablerad mjukvara för dessa (Python, Tensorflow, Keras).

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Göteborg

Artificial Intelligence (AI) studies how computers can accomplish tasks that were traditionally thought to require human intelligence. This course gives an introduction to the subject and has two main purposes. The first purpose is to give an understanding of which sub-areas there are within AI, their historical development and which ethical issues that can arise within different sub-areas. This is done by reading literature within different AI areas, by summarising and discussing the literature in writing, and by reviewing essays by other students.

Read more at UNIVERSITY OF GOTHENBURG

Göteborg

The course gives an introduction to machine learning techniques and theory, with a focus on its use in practical applications. During the course, a selection of topics will be covered in supervised learning, such as linear models for regression and classification, or nonlinear models such as neural networks, and in unsupervised learning such as clustering. The use cases and limitations of these algorithms will be discussed, and their implementation will be investigated in programming assignments. Methodological questions pertaining to the evaluation of machine learning systems will also be discussed, as well as some of the ethical questions that can arise when applying machine learning technologies. There will be a strong emphasis on the real-world context in which machine learning systems are used. The use of machine learning components in practical applications will be exemplified, and realistic scenarios will be studied in application areas such as ecommerce, business intelligence, natural language processing, image processing, and bioinformatics. The importance of the design and selection of features, and their reliability, will be discussed.

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Göteborg

Machine learning is an important subfield of Artificial Intelligence (AI). This course explains what can be done with machine learning and how to use machine learning in chemical engineering. The course provides insight to how calculations are done behind the scene. You learn the topic through a blend of lectures and worked-through examples from the chemical-engineering domain. Lectures illustrate how machine learning is integrated into R&D and production of materials. At the end of the course, you learn how to create predictive models in cases where physiochemical modelling is not feasible. We have selected computer exercises from systems relevant to Chemical Engineers working within research, development, process engineering and production. The exercises are prepared in a way to minimize programming to allow you more time to focus on interpreting the quality of the results.

Read more at CHALMERS UNIVERSITY OF TECHNOLOGY

Göteborg

This course will discuss the theory and application of algorithms for machine learning and inference, from an AI perspective. In this context, we consider as learning to draw conclusions from given data or experience which results in some model that generalises these data. Inference is to compute the desired answers or actions based on the model. The course intends to give a good understanding of this crossdisciplinary area, with a sufficient depth to use and evaluate the available methods, and to understand the scientific literature. During the course we may discuss potential problems with machine learning methods, for example, bias in training data and safety of autonomous agents.

Read more at UNIVERSITY OF GOTHENBURG
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