Applications 2022-12-05
COURSE DESCRIPTION
Big data and the algorithms used in data science, together with the corresponding process and its technology tools, have important implications for addressing climate change. From machine learning algorithms to data visualization, data science methods are used to investigate and better understand climate change and its various effects on land, sea, food, etc.
Data science is a powerful approach which is capable of helping practitioners, and policy-makers understand the uncertainties and ambiguities inherent in data, to identify interventions, strategies, and solutions that realize the benefits for humanity and the environment, and to evaluate the multiple– and sometimes conflicting–goals of decision-makers. In this MOOC course, we introduce methods pertaining to the growing field of data science and apply them to issues relevant to climate change.
Topics
Course content
You will learn
By the end of the course, you will be able to: obtain and analyze datasets; make data-driven decisions; identify and address climate change challenges using data science
Who is the course for?
This course is designed for those who want to improve their analytics and data-driven decision-making skills, with an emphasis on utilizing such skills for addressing climate change challenges. The course will also be useful for practitioners and policy-makers as they can benefit from understanding the uncertainties and ambiguities inherent in data and using it to identify interventions, strategies, and solutions that realize benefits for humanity and the environment.
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.
Örebro University is offering a course in machine learning. The course will offer knowledge of the basic concepts with machine learning, the selection and application of different machine learning algorithms as well as evaluation of the performance of these learning systems. After completing the course, student should be able to prepare data and apply machine learning techniques to solve a problem in an intelligent system. The course is part of the education initiative Smarter at Örebro University.
In the modern IT world, businesses often have access to large amounts of data collected from customer management systems, web services, customer interaction, etc. The data in itself does not bring value to the business; we must bring meaning to the data to create value. Data mining and machine learning is an area within computer science with the goal of bringing meaning to and learning from data. This course will focus on applied machine learning, where we learn what algorithms and approaches to apply on different types of data.This course is for experienced developers working in the industry. The course includes the following: Supervised learning, different types of data and data processing, Algorithms for handling text documents, Algorithms for handling data with numerical and categorical attributes, Neural Networks and Deep Learning for image recognition
To be translated Kursen vänder sig till dig som arbetar inom, eller är intresserad av att förstå tekniskt ledarskap inom industrin och inte har tidigare erfarenhet av att ha ett uppdrag som innebär ledning av andra individer. Du kan vara ny i din roll som arbetslagsledare eller liknande och hanterar gruppnivåer utan personalansvar eller önskar få grundläggande kunskaper som stöttar dig på väg in i ett sådant uppdrag.Då förändringstakten ökar ställer detta krav på ökad flexibilitet och förståelse för hur produktionsprocesser och beslut går till. För att medarbetare och ledare ska kunna utveckla sitt ledarskap för att klara hantera en fullt integrerat miljö med djup förståelse i teknik och förståelse för medarbetare och ekonomi i ett företag, krävs förståelse för individ och grupp samspelar. Kursen är utformad för att kunna få arbeta med problemställningar som finns på arbetsplatsen utifrån ett tekniskt ledarskapsperspektiv där du får diskutera, presentera och beskriva dina förslag till lösningar i ett sammanhang som bygger på vetenskaplig grund som kan användas inom praktiken i industrin. Efter avslutad kurs kan du utförligt redogöra för och diskutera viktiga begrepp, ansatser och ledarstilar inom tekniskt ledarskap för industrin i omställning,presentera och kritiskt diskutera ett praktiskt fall inom området tekniskt ledarskap för industri i förändring och förstå vikten av hastigheten i genomförandet och vikten av att beakta en föränderlig omvärld,diskutera ledarskapets betydelse och ansvar i relation till etiska aspekter av teknikutveckling, hållbarhetsaspekter av teknisk utveckling och jämställdhetsaspekter i tekniska organisationer/grupper. Kurskod, ansökningskodPR024G, HS-21800 Ansökan, behörighet och antagningOm du arbetar inom industrin, men saknar akademiska meriter, kan du ansöka om att bli bedömd på så kallad reell kompetens. Läs mer på den här sidan his.se/ansokindustrikurser
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