DATA3750 Applied AI and Data Science project Course description

In this course, students will acquire an understanding of some of the most important principles of data science and cognitive technologies through project work and online resources. The students will be introduced to fundamental principles of machine learning, data science and artificial intelligence. The main focus will be on how to use these principles to solve industrial tasks by using open-source or other data science platforms. The goal is to provide the students with an introduction to machine learning, data science and artificial intelligence using online resources at the same time as the students solve an industrial problem in the form of a comprehensive project work.

In addition to the projects on offer, students can find their own projects within a relevant company, public organization or nonprofit. In this case, it is the student's responsibility to find a supervisor for the project within the external organization. All student-initiated projects must be approved by a supervisor at OsloMet before the start of the project.

The workload for the project should correspond to two days a week over a twelve-week period during either the Spring or Autumn semester. If the project is completed in the summer, the workload should equal four days a week over a six-week period.

The elective course will only run if a sufficient number of students a registered.

One internal examiner. External examiners are used regularly.

The course provides students with basic knowledge of control and regulation of technical energy systems in buildings. Students will learn basic theory relating to process and instrumentation diagrams (P&ID), block diagrams, different types of sensors, controllers, control devices and actuators. Professional leadership in building automation is included in all topics.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and competence:

Knowledge

The student:

• has a basic technological understanding of the most important concepts in machine learning, data science and artificial intelligence
• has knowledge of the most important methods in machine learning, data science and artificial intelligence
• has knowledge of platforms that can be used to complete major data science projects (for instance IBM Watson’s cloud services)

Skills

The student:

• masters basic data science tools and can extract and visualise information from large quantities of data
• understands the workflow in bigger data science, artificial intelligence or machine learning projects
• is capable of using open-source and commercial tools that are used in industrial projects in the fields of data science, machine learning or artificial intelligence

General competence

The student:

• masters methods and tools used to develop and carry out projects in data science, machine learning or artificial intelligence
• is familiar with the different methods that are used to find the right tools to carry out data science projects
• has an overview of how to visualise and manipulate data and how to develop predictive methods for solving industry problems and other issues relevant to working life

Regular follow-up of the project work by a project supervisor.

The students will work in groups of three to five students to complete a project in data science, machine learning or artificial intelligence in cooperation with relevant external parties such as companies or public organisations.

The supervisor(s) can suggest suitable online courses in AI and data science that the students can take during the first few weeks of the course. The students are also encouraged to take other courses (https://cognitiveclass.ai) that will be useful in order to carry out the chosen project assignment. These courses may, among other things, deal with the following areas: Blockchain, the Internet of Things, Chat Bots, advanced use of data science, etc.

The course can be carried out individually by agreement with the course coordinator.

Projects are selected/distributed at the start of the semester.

Lectures, learning notes and exercises.

None.

Portfolio assessment subject to the following requirements:

• 12 individual learning-/reflection notes
• 3 individual assignment, writing or application of software. Ca 3 timer hver.

One overall grade is awarded for the portfolio.

The exam result can be appealed.

All.

Grade scale A-F.