AI in Business (5 cr)

Code: BUS8PO805-3003

Basic information of implementation

Enrollment
04.01.2021 - 15.01.2021
Enrolment for the implementation has ended.
Timing
18.01.2021 - 21.05.2021
Implementation has ended.
ECTS Credits
5 cr
Campus
Porvoo Campus
Teaching languages
English
Seats
15 - 40
Degree programmes
ABBA Degree Programme in Aviation Business
SAMPO Degree Programme in International Sales and Marketing
TEMPO Degree Programme in Tourism Event and Management
Teachers
Darren Trofimczuk
Groups
PO8
Porvoo Free Choice Studies
VIRTUAL
Virtual implementation
EXCH
EXCH Exchange students
Course
BUS8PO805

Evaluation scale

H-5

Schedule

All online as a virtual course - Moodle

Implementation methods, demonstration and Work&Study

a) Learning with contact teaching including assignments, project work, independent study and exams
b) Virtual learning including virtual study with assignments, project work via the Internet, project execution as a webinar and exams:

Materials

There are five key topics covered in the course module:
Topic 1: An Introduction to Artificial Intelligence
Topic 2: Machine Learning in Business and applications-(with Python code)
Topic 3: Robotics in Business
Topic 4: Artificial Intelligence in Business and Society
Topic 5: The Future of Artificial Intelligence

Recommended literature:
E-Books
Course materials
Webinars
Internet based resources
Course videos & interviews

Teaching methods and instruction

This course introduces the impact of AI in business and introduces students to a number of AI topics. Much of the course is taught virtually, but there will be a few contact classes at Porvoo campus. Guest lecturers/experts on AI will also provide some lectures and these will be recorded as a webinar and placed onto the course. The contact classes and guest lectures will also include a small number of assessment tasks.

Working life connections

Possible company visits and guest lecturers and workshops from different companies and organisations.

Possibility to?operate?with international teams?and international lectures. Possible guest lecturers from international organisations and partner universities.?

Exam dates and re-exam possibilities

a) Learning with contact teaching including assignments, project work, independent study and exams
b) Virtual learning including virtual study with assignments, project work via the Internet, project execution as a webinar and exams:

Assessment Types for each topic:

Pre-Assignment - Pass/Fail
Topic 1: Online Exam -30% (compulsory assignment) -Graded
Topic 2: Machine Learning task with Data file & BigML-30% (Pass = Grade 5, Fail = 0)
Topic 3 & 4: Written Assignment-40% (compulsory assignment) Graded
Topic 5: Forum contribution & 1 x webinar participation– (Pass/Fail)

Learning assignments

Topic 1: Online Exam -30% (compulsory assignment) -Graded
Topic 2: Machine Learning task with Data file & BigML-30% (Pass = Grade 5, Fail = 0)
Topic 3 & 4: Written Assignment-40% (compulsory assignment) Graded
Topic 5: Forum contribution & 1 x webinar participation– (Pass/Fail)

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