Applications 2023-10-17 - 2024-03-03
COURSE DESCRIPTION
Genom denna kurs får du ökade kunskaper om datadriven tjänsteinnovation. Du får lära dig mer om hur du kan använda systematisk insamling av användardata som grund för tjänsteutveckling och innovation.
I den här kursen får du lära dig mer om systematisk datainsamling, analys av användardata samt hur företag och organisationer kan använda insikter från detta arbete vid tjänsteutveckling och innovation. Målet är att ge dig ökad kunskap om tekniska verktyg, analysmetoder och etiska samt legala övervägande som ligger till grund för en systematisk insamling av användardata och hur det på ett strukturerat sätt kan utgöra en grund för tjänsteutveckling och innovation.
Kursen behandlar teorier och modeller där olika typer av scenarios för datainsamling och analyser kommer att presenteras och reflekteras av dig utifrån din verksamhet eller tidigare erfarenheter.
Kursen består av fyra moduler där vi fokuserar på:
Du lär dig att
Efter genomgången kurs kommer du ha en ökad förståelse för:
För vem?
För dig som arbetar med eller är intresserad av datadriven innovation och vill lära dig mer om systematisk datainsamling, analys av användardata samt hur företag och organisationer kan använda insikter från detta arbete vid tjänsteutveckling och innovation.
Information och anmälan
Undervisningen genomförs på distans via Canvas som är Karlstads universitets lärplattform. Kursen är på avancerad nivå och ger 5 högskolepoäng.
Kursinnehållet baseras på forskning och är utvecklat i dialog med näringslivet. Kursen är avgiftsfri. Antal platser är begränsat.
Anmäl ditt intresse till kursen via kursens webbplats.
Välkommen!
The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called ‘Big data’ being produced every day at exponential growth. The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.
The aim of this course is to provide participants with the principles behind model-driven development of software systems and the application of such a methodology in practice. Modelling is an effective solution to reduce problem complexity and, as a consequence, to enhance time-to-market and properties of the final product.
The 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 modeling
In this course you will learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.
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.
The course is broken down into: Basic Bayesian concepts Selecting priors, deriving some equations Bayesian inference, Parametric model estimation Sampling based methods Sequential inference (Kalman filters, particle filters) Approximate inference, variational inference Model selection (missing data) Bayesian deep neural networks