Intelligent process automation (5cr)

Code: 5W00FK35-3001

General information

Enrolment period: 02.07.2021 - 18.09.2021; Registration for the implementation has ended.

Timing: 30.08.2021 - 27.11.2021; Implementation has ended.

Credits: 5 cr

Mode of delivery: Contact learning

Unit: MD in Automation in Smart Industry

Campus: TAMK Main Campus

Teaching languages: Finnish

Degree programmes: Master's Degree Programme in Automation in Smart Industry

Teachers: Pasi Airikka

Person in charge: Anne Seppälä

Course: 5W00FK35

Objectives (course unit)

A student understands a definition of intelligent process automation with a set of different functionalities. A student recognises applications of intelligent process automation and understands its applicability, limitations and achievable benefits. A student can engineer some of the functionalities of intelligent process automations.

Content (course unit)

Intelligent process automation with
- its description and definition
- methods and functionalities
- principles, limitations and achievable benefits
- applications and use cases

Assessment criteria, satisfactory (1-2) (course unit)

A student understands a definition of intelligent process automation. A student recognises some functionalities of intelligent process automation. A student recognises an application of intelligent process automation.

Assessment criteria, good (3-4) (course unit)

In addition to the criteria listed above, a student understands several functionalities of intelligent process automation with limitations and achievable benefits. A student recognises applications of intelligent process automation.

Assessment criteria, excellent (5) (course unit)

In addition to the criteria listed above, a student widely understands and recognises functionalities, limitations and achievable benefits of intelligent process automation. A student recognises several applications of intelligent process automation and can engineer applications of intelligent process automations.

Location and time

Information on virtual classes is given in the beginning of the course and on Moodle. Information on timing is available via Lukkari.

Exam schedules

There is no exam in the course. A failed course can be passed by completing all the exercises given in the course.

Assessment methods and criteria

Student performance assessment is based on the given and qualified exercises.

Assessment scale

0-5

Teaching methods

Virtual teaching, home excercises (individual and/or working group), self-learning.

Learning materials

Lectures, notes made by a student, other referred material by a lecturer

Student workload

The total estimated working hours for succesfully passing the course is ca. 133 hours (5 cr x 1600/60 h/cr) of which a student has to allocate a major part for self-learning.

Content scheduling

The more detailed course structure is introduced in the first class and it will be available in the course information on Moodle afterwards.

Completion alternatives

None.

Practical training and working life cooperation