AI in Business (5 cr)
Code: BUS8PO805-3001
Basic information of implementation
- Enrollment
- 17.06.2019 - 23.08.2019
- Enrolment for the implementation has ended.
- Timing
- 19.08.2019 - 13.12.2019
- 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
-
PO8Porvoo Free Choice Studies
-
EXCHEXCH Exchange students
- Course
- BUS8PO805
Evaluation scale
H-5
Schedule
Contact classes will run every other week.
50% Virtual tasks
50% contact classes
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
The main book to use for this course is: Artificial Intelligence: 101 Things You Must Know Today About Our Future, Lasse Rouhiainen, ISBN: 1982048808
Materials will be provided in the course such as:
• Learning materials
• Books on the content topics
• E-books and online articles
• Online tutorials
• Companies’ web portals
• Relevant media, news agencies, quality press, etc.
• Exercises, tests
Instructors’ own materials, materials produced by students.
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.
• Learning methods
• Virtual classes
• Inquiry learning
• Peer-to-peer learning
• Tutorials
• Possible company visits
• Lectures and workshops
• Independent study and teamwork
Working life connections
Possible company visits and guest lecturers and workshops from different companies and organisations.
Exam dates and re-exam possibilities
All assessment will be done in the course either in contact lessons (through assignments) or via Moodle quizz/exams.
• Virtual course assignments x3 (60%)
• Guest lectures and webinar tasks (40%)
Internationality
Possibility to operate with international teams and international lectures. Possible guest lecturers from international organisations and partner universities.
Completion alternatives
Half the course will be virtual, so opportunities to complete some assignments via this method.
Learning assignments
Contents
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
Grade 1
The student has some understanding of AI in business used in the module. He/She is able to communicate at a minimal level with AI in business terminology and theory. The student is able to operate only when aided by other students and supervisors.
Grade 3
The student is able to understand most concepts in AI in business within the module. He/She is able to communicate at an intermediate level with AI in business terminology and theory. He/She is able to discuss and write assessment tasks with reference to some AI academic materials mostly independently.
Grade 5
The student is able to understand concepts in AI in business within the module. He/She is able to communicate fully independently with AI in business terminology and theory. He/She is able to discuss and write assessment tasks with reference to AI academic materials independently. He/She applies an entrepreneurial problem-solving approach to their project work.
Assessment methods
• Virtual course assignments x3 (60%)
• Guest lectures and webinar tasks (40%)
Detailed assessment can be checked from implementation plans (including attendance requirements). The self-assessment of one's own learning does not influence the module grade. The self-assessment and students’ feedback to the module will be used for the module development. The feedback is collected in an electronic form.