Introduktion till tillämpad maskininlärning

Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools used in a wide variety of areas such as finance, healthcare, e-commerce, industry, entertainment and many others to automate numerous tasks. For example, in healthcare an ML algorithm can be used to predict a diagnosis based on the patients data. An autonomous vehicle can employ an AI algorithm to decide whether to apply emergency breaking. Fraud and money laundering can also be successfully identified by AI and ML technology when used by a financial institution. Successful deployment of this technology requires understanding of application areas of different AI and ML algorithms and their performance characteristics.The course introduces basic skills required to design and build simple machine learning solutions using Python as the programming language and the relevant Python libraries. In particular, the course focuses on both supervised and unsupervised machine learning including such algorithms as Linear Regression, Support Vector Machines, Decision Trees, Ensemble Learning, K-Means, and DBSCAN. During the course, the students will learn to select the right ML algorithm for the task and optimize its parameters in order to achieve desired performance.The target group for the course is senior and medium level management as well as engineering and technical personnel unfamiliar with AI and ML technology.Industry and academyThis course has been developed in collaboration with industry partners within the framework of one of our educational projects. The project aims to provide courses at the advanced level to a mix of professionals and students to strengthen the Swedish industry's competitiveness.We apply validation of work experience to see if you have knowledge that corresponds to the eligibility requirements for the course. Do not forget to attach a description if you are applying based on work experience, a form is available on our website www.hv.se/produktionskurser. SCHEMA3 sep kl 09.15-12.00 ZOOM9 sep kl 13.15-15.00 ZOOM 15.15-17.00 LAB online16 sep kl 13.15-15.00 ZOOM 15.15-17.00 LAB online23 sep kl 13.15-15.00 ZOOM 15.15-17.00 LAB online1 okt kl 13.15-15.00 ZOOM 15.15-17.00 LAB online10 okt kl 09.15-12.00 valbar Campus 11 okt kl 14.15-17.00 valbar ZOOM Preliminärt schema. Från kursstart gäller schema uppdaterat på kursens Canvassida.