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Artificial Intelligence

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Grunderna i AI, del 2: att utveckla AI

KursinnehållKursen syftar till att ge en introduktion till praktisk AI, med fokus på grundläggande maskininlärning. Syftet är att förstå hur man kan skapa AI-system med hjälp av maskininlärning, få inblick i teknikens möjligheter och begränsningar, samt få en överblick över vanliga metoder för maskininlärning. Börja läsa när du villDu kan börja läsa kursen i stort sett när du vill då kursen är en online-kurs med flexibel antagning. Du gör ansökan till den termin du tänker börja läsa kursen. Vill du börja direkt så ansöker du till innevarande termin. Termin väljer du här ovan, så kommer du till rätt ansökningstillfälle.  KursformatKursen är en distanskurs som görs i egen takt och hanteras i sin helhet i en web-baserad kursmiljö. Kursen baseras på självstudier av kursmaterialet och examineras med självrättande tester och inlämningar. Du som har gjort Elements of AI, Part 2: Building AI kan anmäla dig till den här kursen för att få dina resultat validerade. Detta innefattar att göra ett valideringstest med frågor motsvarande de som finns i Elements of AI, Part 2: Building AI för att säkerställa att det verkligen är du som gått igenom kursen. För mer information se denna länk. Kursen handleds över internet. Information om behörighetObservera att du vid ansökan till kursen måste kunna styrka att du har grundläggande behörighet. Om dina gymnasiemeriter inte redan finns på dina sidor på antagning.se så behöver du ladda upp gymnasieexamen, eller motsvarande, på antagning.se i samband med din ansökan.

Logical Problem Solving with Answer Set Programming

Answer Set Programming (ASP) is a declarative programming paradigm designed within the field of Artificial Intelligence (AI), and used to solve complex search-problems. The declarative nature of ASP allows one to encode a problem by means of logic. In this way, unlike in imperative programming approaches, there is no need to design an algorithm as a solution for the given problem. In this sense, ASP is comparable with SAT-based encoding or constraint satisfaction problems. However, due to its stable-model semantics, ASP provides a richer representation language useful to handle uncertain situations more effectively for real world scenarios. The advantages of declarative programming together with non-monotonic nature of ASP in handling uncertainties have recently made ASP more attractive both for academia and industry. This course focuses on formalizing and solving various search problems in planning, scheduling and system configuration in ASP.

Smart Healthcare with Applications

The course is part of the programme MAISTR (hh.se/maistr) where participants can take the entire programme or individual courses. The course is for professionals and is held online in English. Application is open as long as there is a possibility of admission. The courses qualify for credits and are free of charge for participants who are citizens of any EU or EEA country, or Switzerland, or are permanent residents in Sweden. More information can be found at antagning.se. About the course Smart Healthcare with Applications, 4 credits Who is this course for?The course suits you with any Bachelor’s degree (equivalent of 180 Swedish credit points / ECTS credits at an accredited university) who have an interest in applying Artificial Intelligence (specifically Machine Learning) to healthcare. Leadership/management experience in health-related organization/industry OR a Bachelor degree in computer science is advantageous. What will you learn from this course?Healthcare as a sector together with other health-related sources of data (municipalities, home sensors, etc.), is now in a place and can take advantage of what data science, Artificial Intelligence (AI), and machine learning (ML) have to offer. Information-driven care has the potential to build smart solutions based on the collected health data in order to achieve a holistic fact-based picture of healthcare, from an individual to system perspective. This course aims to provide a general introduction to information-driven care, challenges, applications, and opportunities. Students will get introduced to artificial intelligence and machine learning in specific, as well as some use cases of information-driven care, and gain practice on how a real-world evidence project within information-driven care is investigated. What is the format for this course?Instruction type: The lectures, announcements, and assignments of this course will be fully online via a learning management system and presented in English. Each lecture is delivered through a video conference tool with a set of presentation slides displayed online during each class session. Online practical labs (pre-written Python notebooks) are also provided in the lectures.

Under the Hood of Machine Learning

During this free course, you will learn the basics of AI, machine learning and Neural Networks. You will learn about important concepts such as Gradient Descent, Backpropagation and Activation Functions. Common metrics are presented as well as problems such as overfitting and underfitting. Among other topics, we go in-depth on: What are Neural Networks and how are they trained? How do Neural Networks analyze images and text? How important is data? See all free online courses that KTH offers