The course provides an introduction to self-service businessintelligence. The course focuses on increasing awareness around managing challenges when implementing and using self-service business intelligence. The assignmentscontain an account and discussion to be able to succeed with an investment within self-service businessintelligence.
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.
In the modern IT world, businesses often have access to large amounts of data collected from customer management systems, web services, customer interaction, etc. The data in itself does not bring value to the business; we must bring meaning to the data to create value. Data mining and machine learning is an area within computer science with the goal of bringing meaning to and learning from data.
This course will focus on applied machine learning, where we learn what algorithms and approaches to apply on different types of data.This course is for experienced developers working in the industry. The course includes the following: Supervised learning, different types of data and data processing, Algorithms for handling text documents, Algorithms for handling data with numerical and categorical attributes, Neural Networks and Deep Learning for image recognition
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.
The promise of intelligent robot systems is that they can accomplish more tasks, more efficiently than a single-purpose industrial robotic solution. Intelligent robots act competently because they can plan, sequence and enact the actions that are appropriate in the context in which they find themselves. In order to achieve this capability, intelligent robots use Artificial Intelligence (AI) Search Methods. These are general-purpose algorithms for solving combinatorial problems, in other words, they constitute a robot's "reasoning engine". This course introduces students to the most important types of AI search methods. These are then instantiated in three industrially-relevant application contexts, namely, resource scheduling, motion planning, and multi-robot coordination.
As AI systems become more common and expand their abilities, the decisions they made have a crucial impact on society as a whole. Whether they are designed to recommend content or product online, to assist judges or physicians in their decision-making, or to decide how to distribute mortgages or video surveillance cameras, these systems can have a crucial and lasting impact on all of us. For this reason, it is of paramount importance that those in charge of designing such systems work toward ethical and responsible systems.
This course covers the theoretical and practical aspects of normative ethics and how they apply to AI systems, discuss how AI systems can become biased, as well as how to prevent and correct possible bias.
Through concrete examples, case studies, and project, this course aims at raising awareness on the problem of ethical AI as well as giving the students practical experience on how to ensure ethical and responsible development of AI systems in their everyday work.
This course is given on-line with three mandatory Zoom-meetings.
This course is directed towards working professionals.
The purpose of the course “Artificial Intelligence for Managers” is to give managers and decision makers a principle understanding of AI and to increase their understanding of opportunities, difficulties, benefits, and risks connected to AI. It is neither an “Introduction to AI” nor an “AI for dummies” course. Instead, it is set to demystify AI and to transform it into an actionable tool for manages and decision makers.
Target groupThis course is for product managers, project managers, executives, and engineering managers in organizations that have already made, or are about to make, the transition to working with AI.
ContentThe course is organized in three modules. The initial module will focus an introduction to AI, giving an understanding of what type of cases can be addressed with AI and what managers need to know about AI technology. Module two will cover tools and concrete on how to set up an AI strategy and roadmap, how to get started on AI projects, how to integrate AI and IT development, how to (self) evaluate AI in use, and, not to forget, the ethical and legal aspects of AI. The third module will give the participants the chance to use their new knowledge and tools and work with their own practical cases and how they could be addressed using AI.
The goal of the course is to empower the participants to:
Describe the principal concept of AI, its strengths, and shortcomings
Understand opportunities, myths, and pitfalls of AI
Identify problem areas in industry, society, and in management where AI could be utilized
Analyze how AI can be applied in a particular problem area
Manage an AI strategy and get started: implement a strategy and a roadmap to apply AI in a particular problem area
Understand how to integrate AI with IT development
Assess the maturity of AI utilization in an organization
Reflect on applications of AI from an ethical and legal perspective as well as the future challenges (technical, organizational, social, etc.)
Practical informationAll materials will be accessible and include reading material, lecturer slides etc. The lectures can either be attended live via Zoom or later using the recordings at a time that is convenient for the participants. There will be 3 onsite workshops with a focus on interaction with the teacher and the co-participants of sharing real-life experiences and insights. The course will be delivered in a flexible manner to facilitate the combination of course work with your ongoing professional commitments.
The total effort to pass this course is typically around 200 hours.
Teaching language: English
Entry requirementsThe basic eligibility for this course is a bachelor’s degree. Candidates with corresponding work experience are also invited to apply. Two years of relevant work experience is considered equivalent to one year of university studies at bachelor level.
The course is free