The course aims to provide knowledge and skills in modern techniques and tools for machine learning, to create an understanding of its uses, strengths, and weaknesses. All in order to then apply these skills in a real world application.
After the course, you will be able to: - describe basic terms, methods and approaches for machine learning - compare and argue for and against different types of machine learning methods for different application areas - use basic methods and various tools for machine learning - apply these techniques in a real world application as well as reflect and evaluate the results
Course Content - Basic terms, methods and approaches for machine learning - Overview of different types of machine learning algorithms - Uses, strengths and weaknesses of the various types of machine learning - Overview of common tools and their applications - Neural networks (NN), convolutional neural network (CCN), recurrent neural networks (RNN), long short term memory (LSTM) - Machine learning project
The course consists of a few lectures, a web-based theory exam, a series of laboratory exercises and a project assignment. The lectures present the necessary theory, tools, algorithms and basic terms, etc. The web-based theory exam consists of a quiz intended to examine basic terms and understanding. The labs are intended to provide basic skills and tools for machine learning. Finally, in the project, the student will demonstrate their own work on machine learning to merge previous knowledge. Depending on the student's previous experience in machine learning and programming ability, the work effort is estimated to be 80 hours of work.
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.
Today, the explosion of data has created new opportunities to apply machine learning (ML). Handling of the large amounts of data created by the very rapid digitization would not be possible without Machine Learning (ML). The purpose of the course "Introduction to Machine Learning" is to give you the foundation for ML. You will get an introduction to the basic areas of ML: data, statistics and probability for ML.
In contrast to learning how to do manual testing, in this course you will learn how to generate tests automatically in the sense that test creation satisfying a given test goal or given requirement is performed automatically.
This course provides an understanding of automating software testing using program analysis with the goal of intelligently and algorithmically creating tests. The course covers search-based test generation, combinatorial and random testing while highlighting the challenges associated with the use of automatic test generation.
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 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