This is a report about machine learning in the field of computer science. The problem handled is prediction of energy consumption in district heating systems. Prediction of energy consumption in district heating systems is a delicate problem because of the social behaviours, weather and distribution time that has to be accounted for. One algorithm is introduced and three different experiments are made to determine if the algorithm is useful. The results from the experiments were good. This report differs in approach to the problem then other reports found in this field. The difference is that this report tries to handle social behaviours and looks at a decentralized view of the problem instead of centralized. / Denna rapport är om maskininlärning och hur mna kan använda en maskinlärningsalgoritm för att förutspå konsumption i fjärrvärmenät. Rapporten skiljer sig markant i synsätt jämt emot andra rapporter i ämnet genom att den tittar även på de sociala faktorerna.

Fully autonomous vehicles are expected to revolutionize transportation, reduce the cost of ownership, contribute to a cleaner environment, and prevent the majority of traffic accidents and related fatalities. Even though promising approaches for achieving full autonomy exist, developers and manufacturers have to overcome a multitude of challenged before these systems could find widespread adoption. This multiple case study explored the strategies some IT hardware and software developers of self-driving cars use to adapt traditional vehicle design frameworks to address consumer and regulatory requirements in autonomous vehicle designs. The population consisted of autonomous driving technology software and hardware developers who are currently working on fully autonomous driving technologies from or within the United States, regardless of their specialization. The theory of dynamic capabilities was the conceptual framework used for the study. Interviews from 7 autonomous vehicle hard and software engineers, together with 15 archival documents, provided the data points for the study. A thematic analysis was used to code and group results by themes. When looking at the results through the lens of dynamic capability theory, notable themes included regulatory uncertainty, functional safety, rapid iteration, and achieving a competitive advantage. Based on the findings of the study, implications for social change include the need for better regulatory frameworks to provide certainty, consumer education to manage expectations, and universal development standards that could integrate regulatory and design needs into a single approach.

The diploma thesis deals with artificial intelligence utilization for predictions on stock markets.The prediction is unconventionally based on Bayes' probabilistic model theorem and on its based Naive Bayes classifier. I the practical part algorithm is designed. The algorithm uses recognized relations between identifiers of technical analyze. Concretely exponential running averages at 20 and 50 days had been used. The program output is a graphic forecast of future stock development which is designed on ground of relations classification between the identifiers