Web application security encompasses that the student should learn to understand and discover weaknesses and vulnerabilities in web applications both on the server side and on the client side as well as be able to develop solutions for protection and conduct tests.
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.
ROS (Robot Operating System) is a common set of tools used in academia to do research within autonomous systems. It shortly provides a middleware for handling communication, as well as interfacing sensors and actuators, visualization, simulation and datalogging and infrastructure where it is easy to share your own methods and algorithms. The latter has allowed a large set of different of state-of-the-art research approaches to be readily available for downloading. Due to its popularity it is also getting more widespread in the industrial community, especially in R&D. This course will give you hands-on experience how to utilize these tools and apply them to a problem of your choice.
Maybe you want to connect monitoring to a truck, or why not build a connected pump control? Do you want to measure temperatures, pressures or vibrations? Do you want knowledge about how to connect one of your existing products? Then this course is for you.
Do you work as an engineer in the industry and want to learn how to develop an idea with IoT? Do you work as a developer at an IT company and want to learn more about the hardware and the entire infrastructure within IoT? The course is primarily aimed at those who are professionals in the engineering profession, but you do not need to be either a programmer or an electronics engineer to take advantage of the course. The content is adapted so that you can work with your specific ideas.
The course is focused on providing both theoretical and practical knowledge in the field of Internet of Things. You will gain knowledge of the area's applications and definitions, and you will learn how to build an IoT device, all the way from hardware to visualization. You will have the opportunity to practically work with hardware, sensors, as well as infrastructure and security. We will work with, among other things, WiFi, BLE, LoRaWAN, SigFox, NB-IoT / LTE-M1, as well as insight into how data is transported throughout from the device to the database and then to the application.
The course will be held mostly at a distance with a couple of scheduled workshops (13/9, 6/10, 27/10) either on site or online. All lectures will be available online. The course will be delivered in a flexible way to facilitate the combination of coursework with your ongoing professional commitments.
You will need to buy IoT hardware before the start of the course, the cost can be different depending on the type of project, guide value is approx: SEK 1,000.
The total scope of the course is normally about 80 hours.Language of instruction: EnglishThe course is free of charge
The course is part of the programme MAISTR (hh.se/maistr) where participants can take the entire programme or individual courses. The course is for professionals and is held online in English. Application is open as long as there is a possibility of admission. The courses qualify for credits and are free of charge for participants who are citizens of any EU or EEA country, or Switzerland, or are permanent residents in Sweden. More information can be found at antagning.se.
About the course Smart Healthcare with Applications, 4 credits
Who is this course for?The course suits you with any Bachelor’s degree (equivalent of 180 Swedish credit points / ECTS credits at an accredited university) who have an interest in applying Artificial Intelligence (specifically Machine Learning) to healthcare. Leadership/management experience in health-related organization/industry OR a Bachelor degree in computer science is advantageous.
What will you learn from this course?Healthcare as a sector together with other health-related sources of data (municipalities, home sensors, etc.), is now in a place and can take advantage of what data science, Artificial Intelligence (AI), and machine learning (ML) have to offer. Information-driven care has the potential to build smart solutions based on the collected health data in order to achieve a holistic fact-based picture of healthcare, from an individual to system perspective. This course aims to provide a general introduction to information-driven care, challenges, applications, and opportunities. Students will get introduced to artificial intelligence and machine learning in specific, as well as some use cases of information-driven care, and gain practice on how a real-world evidence project within information-driven care is investigated.
What is the format for this course?Instruction type: The lectures, announcements, and assignments of this course will be fully online via a learning management system and presented in English. Each lecture is delivered through a video conference tool with a set of presentation slides displayed online during each class session. Online practical labs (pre-written Python notebooks) are also provided in the lectures.