Örebro University is offering an introduction course in artificial intelligence. The course will address the basic concepts within classical artificial intelligence (other than machine learning). Traditional artificial intelligence is characterized by the so-called declarative approach to problem solving. The course deals with a selection of different intelligent problem-solving methods, both in theory and practice. After completing the course, the student will be able to model and use appropriate generic solution algorithms to solve problems in an intelligent system. The course is part of the education initiative Smarter at Örebro University. Read more
The emergence of artificial intelligence has created a new opportunity to apply machine learning (ML) in industry 4.0. In this era of the Internet of Things and Big data, processing of a large amount of data would not be possible without ML. Thus, ML made industrial production smarter than ever before. So, learn the course “Machine learning for Industry 4.0” to bring in new business models in your company and boost productivity using ML. The course provides knowledge about basics of ML and data, describes ML algorithms and tools and also explains the concept of Industry 4.0 and digitalization in industry 4.0. The individual processes can be better understood and optimized with the help of the knowledge from the course. Also, it could have an important contribution in analysing the industrial data set, improving results, and making decision and/or predictions for failure, demand, sales, and production. This course is a collaboration between Högskolan Väst, Linnéuniversitetet and Mälardalens Högskola. It consist of three course modules: Introduction to Machine Learning (MDH – 2 credits, Basic level), Industry Digitalization – Industry 4.0 (HV – 2,5 credits, Advanced level), Applied Machine Learning (LNU – 3 credits, Advanced level). Students who pass all three courses will receive a diploma from Learning for Professionals.
Producing good machine learning models using the algorithms discussed in Introduction to Machine Learning – Part I typically depends on proper usage and preprocessing of the data, as well as the ability to interpret and act on the results generated by the method. Thereto, you will learn in this course more about the practical side of applying the machine learning techniques to actual problems including feature extraction methods, to figure out which part of the data is relevant to your problem, the bias-variance dilemma, to figure out if you have enough data for your model, ensemble learning, for combining more than one machine learning technique, and other experience-based practical recommendations. Armed with this knowledge, you will have to solve a classification problem in competition with the rest of the class, getting a hands-on experience in using Machine Learning.
Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. The course is part of the education initiative Smarter at Örebro University. This is course requires completion of course Reinforcement Learning part 1. Read more