Introduction to Applied Machine Learning

Education facts

Code: ITM600

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 patient’s 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 academy This 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](<https://www.hv.se/produktionskurser>). ## SCHEDULE From the start of the course, the updated schedule will apply on the course's Canvas page.

Entry requirements

Passed courses of 90 HE credits within the field of technology with at least 15 HE credits on level 61-90 HE credits or equivalent. Passed courses of 2,5 HE credits within the field of machine learning and AI. Passed courses of 7,5 HE credits within the field of Python programming.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Introduction to Applied Machine Learning

  University West

  TROLLHÄTTAN

  Start date: 15 March 2027

  End date: 25 April 2027

  Pace of study: 30 %