Opinto-opas

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

+ Course book: (preplan)

Artificial Intelligence: A Modern Approach
Authors: Stuart Russell and Peter Norvig
Publisher: Pearson; 4th Edition (April 28, 2020).

Note: the instructor will cover some parts of this book according to the course topics and instructions, which will be announced in the first session.

+ The course also has some extra reading material, videos, and slides which will be announced during the course and available via the learning environment (ITS).

Opetusmenetelmät

+ Lectures: The instructor will deliver approximately 10 lectures on the course topics using slides, videos, exercise, seminar, and final project demonstrations. In addition, 3 lectures will be delivered by our guest lecturers from TUAS different research groups or external companies to cover the applications of AI in autonomous driving and wireless communications, healthcare, gaming, and business.

+ 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 2-4 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

+ Final seminar in Week 16.

Opiskelijan ajankäyttö ja kuormitus

+ Contact hours
- 13 times 3h theory and practice: 13 x 3h = 39hours
+ Home work: approximately 69 hours
+ Seminar: approximately 25 hours

Total: approximately 130 hours

Sisällön jaksotus

Course Topics and Scheduling (pre-planning):
week 03: Introduction to AI: history, definitions, and applications.
week 04: Problem-solving methods in AI and Intelligent Agents
week 05: State Space Search Algorithms
week 06: Exercise Demonstrations I
week 07: Basics of Machine Learning
week 08: Winter Break
week 09: Data preprocessing for ML algorithms
week 10: Supervised Learning: training and evaluation of some supervised ML models like linear regression and logistic regression.
week 11: Ethical and Social Implications of AI + Exercise Demonstrations II
week 12-14: Applications of AI in Real-World Problems
week 15: Independent teamwork for the final seminar
week 16: Final seminar presentation

Viestintäkanava ja lisätietoja

Additional information is share via ITS

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

You can achieve points from participation, exercises, and final seminar:
- 20% points from participation
- 50% points from practical exercises
- 30% points from the final seminar

Assessment:
- Participation and exercise (Part 1)
- Final seminar (Part 2)
Students must achieve at least 50% of the participation and exercises points AND 50% of the 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% of both Part 1 and Part 2 (minimum to pass the course)
2: 60-70%
3: 70-80%
4: 80-90%
5: 90- 100%

Hylätty (0)

Less than 50% points of both parts

Arviointikriteerit, tyydyttävä (1-2)

50 - 69% points

Arviointikriteerit, hyvä (3-4)

70 - 89% points

Arviointikriteerit, kiitettävä (5)

At least 90% points