GRUNDERNA I AI, DEL 2: ATT UTVECKLA AI

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.

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.

Answer Set Programming (ASP) is a declarative programming paradigm designed within the field of Artificial Intelligence (AI), and used to solve complex search-problems. The declarative nature of ASP allows one to encode a problem by means of logic. In this way, unlike in imperative programming approaches, there is no need to design an algorithm as a solution for the given problem. In this sense, ASP is comparable with SAT-based encoding or constraint satisfaction problems. However, due to its stable-model semantics, ASP provides a richer representation language useful to handle uncertain situations more effectively for real world scenarios. The advantages of declarative programming together with non-monotonic nature of ASP in handling uncertainties have recently made ASP more attractive both for academia and industry. This course focuses on formalizing and solving various search problems in planning, scheduling and system configuration in ASP.

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.

To be translated Svensk industri möter nu en av de största utmaningarna sedan den industriella revolutionen. Vad innebär det för dig som ledare eller blivande ledare i industrin? Hur bibehåller du drivkraften hos dina medarbetare?Välkommen till kursen tekniskt ledarskap för framtidens industriprocesser. Den här kursen är särskilt framtagen för dig som har, eller strävar efter att ha en ledande roll i en industriell organisation. Kurser ger dig fördjupad kunskap inom området teknisk ledarskap med hänsyn till både mänskliga faktorn och ekonomin utöver den tekniska baskunskapen, den så kallade fullt integrerade teknikern eller ingenjör. Vi tittar närmare på och diskuterar olika ledaregenskaper och ledarstilar. Vad innebär det att vara en ledare med tekniskt ansvar?Stora förändringar krävs för att svensk industri ska fortsätta vara konkurrenskraftig. Genom studier av praktikfall och arbetsrelaterade fall skapas djupare förståelse för under vilka förutsättningar tekniska ledare arbetar och vilka krav som ställs. Kurskod, anmälningskodVP737A, HS-26353 Ansökan, behörighet och antagningOm du arbetar inom industrin, men saknar akademiska meriter, kan du ansöka om att bli bedömd på så kallad reell kompetens. Läs mer på den här sidan his.se/ansokindustrikurser

The aim of this course is to give students insight about certification and about what it means to certify/self-assess safety- critical systems with focus on software system and to create a safety case, including a multi-concern perspective when needed and reuse opportunities, when appropriate.