Machine learning and Artificial Intelligence project (5cr)

Evaluation scale

H-5

Content scheduling

Contact consultation with teachers when needed.
Times reserved for that purpose; AC or possibly on campus: 29.11.2021 - 17.12.2021

Objective

In this module, you carry out a Machine Learning and Artificial Intelligence project based on a real task from the working life. The project is following the objectives and the requirements defined by the customer. During the project, you analyse the requirements, select the working tools and methods, and implement the project task. The prerequisite for this course is the possession of the solid and comprehensive subject matter knowledge and skills in Statistics, Machine Learning, Artificial Intelligence, and Data Analytics.

Content

Tieto puuttuu

Location and time

29.11.2021 - 17.12.2021

Materials

We will probably use Anaconda, so all freely available material related to that is recommended. Other tools may be used also.

Teaching methods

Problem-based and team based learning is applied. Students will seek information and solve problems related to the project work issued. While this is about applying what has been learned earlier, coaching related to project issues that raise is orcourse available. Conventions from selected areas in software industry are used. In the start of the cource there will be "kick off" meeting where project subject(s) are introduced and team(s) formed. Access to subject matter experts related to project(s) will be ensured as well as is possible to simulate real project situation. Teachers prepare the setting for learning and provides coaching for the students. Teaching/coaching sessions may take place on campus and online. The main focus will be on knowledge searching and practical work on it.

Employer connections

Software industry conventions are used. This course may include a case company selected by the university. In addition, students are able to propose your own case companies, whose business information and data analytics they would like to develop. Students must provide a free form commission agreement from their own case companies.

Completion alternatives

Before the course starts, students may propose to the course teachers their personal implementation plan. The plan must be realistic and result in verificable development in the targeted competence(s). In addition, guidance from MIGRI and student visa must be taken into account. Course teachers accept or reject student's plan based on their own consideration.

Student workload

The student's estimated workload of this implementation is 135 h as follows:
Roughly half is Independent individual and teamwork guided when needed.
Rest of hours will be mostly learning the subject with guided practical and knowledge seeking exercises.

Assessment criteria, satisfactory (1)

Evaluation target: You apply the knowledge of Machine Learning (ML) algorithms and Artificial Intelligence (AI) in the development project.

Satisfactory
You understand the main activities of the project and can implement simple project tasks with supervision and guidance.

Assessment criteria, good (3)

Good
You specify the project activities according to the given requirements and can choose appropriate tools and methods.

Assessment criteria, excellent (5)

Excellent
You understand comprehensively the project activities and can implement them independently. You can also evaluate different alternatives and can choose an effective implementation according to the project objectives and requirements.

Qualifications

NULL