Introduction to Artificial Intelligence (5op)

Arviointiasteikko

H-5

Sisällön jaksotus

Detail of course scheduling will be announced later during the lectures.

Tavoitteet

After completing the course, the student is able to:
- Understand what the artificial intelligence is
- Explain the basic concepts of artificial intelligence
- Describe the fields of artificial intelligence
- Describe the machine learning process

Sisältö

Introduction to artificial intelligence (AI)
Problem solving and search algorithms
Introduction to machine learning
Ethical and social implications of AI

Oppimateriaalit

The course materials will be announced later during the course and will be available via the learning environment (ITS).

Opetusmenetelmät

+ Lectures: The instructors will deliver approximately 10 lectures on the course topics

+ Assignments: Students will complete several assignments that involve problem-solving, critical thinking, and programming exercises.

+ Seminar presentation: Students will present a 15-minutes seminar in a group of 3-4 about the Future of AI including current trends and advancements in AI, AI in research and development, Ethical, legal, and societal challenges in the future.

Tenttien ajankohdat ja uusintamahdollisuudet

+ No exam

+ The final seminar will be presented by the teams during the last week of the course. Retakes are not possible.

Pedagogiset toimintatavat ja kestävä kehitys

The perspective of sustainable development is considered in the course content

Toteutuksen valinnaiset suoritustavat

Not any alternative method

Opiskelijan ajankäyttö ja kuormitus

+ Contact hours

- 13 times 2h theory, practice, and independent work: 13 x 2h = 36h
+ Self-studies and homework assignments: approximately 68h
+ Final seminar: approximately 31 hours

Total: approximately 135 hours

Arviointimenetelmät ja arvioinnin perusteet

You can achieve points from individual assignments (homework or classwork) and the teamwork final seminar:
- Part 1: Individual assignments (70%)
- Part 2: Teamwork final seminar (30%)

Students must achieve at least 50% of the individual assignments points AND 50% of the teamwork final seminar points to pass the course.

The course is graded on a scale of 0-5.

Grading will be according to the total points collected by the student during the course.

1: 50% (minimum to pass the course)
2: 60-69%
3: 70-79%
4: 80-89%
5: 90- 100%


Use of AI in assignments: USE OF AI REPORTED.
AI can be used in the creation of outputs, but student must clearly report its use. Failure to disclose the use of AI will be interpreted as fraud. The use of AI may affect to assessment.

Hylätty (0)

The student did NOT get at least 50% of the points in Part 1 OR did not get at least 50% of the points in Part 2.

Arviointikriteerit, tyydyttävä (1-2)

1: 50% - 59% from the total points

2: 60% - 69% from the total points

Arviointikriteerit, hyvä (3-4)

3: 70% - 79% from the total points

4: 80% - 89% from the total points

Arviointikriteerit, kiitettävä (5)

90%- 100% from the total points

Lisätiedot

+ Communication Channel: Itslearning on-site during contact teaching

+ Qualifications/Prerequisites:
Student enrollment in the course will not be accepted by the instructor if they have not passed the following prerequisite courses:
- Data Structures and Algorithms
- Python Programming

+ The other additional information is share via ITS