Introduction to Artificial Intelligence (5 cr)

Evaluation scale

H-5

Content scheduling

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:
week 08: Winter Break
week 09:
week 10:
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

Objective

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

Content

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

Materials

+ 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).

Teaching methods

+ Lectures: The instructor will deliver approximately 10 lectures on the course topics using slides, videos, exercise, seminar, and final seminar presentation. In addition, 3 lectures might 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.

Exam schedules

+ No exam, retake not possible after the publication of the final assessment/course grade

+ Final seminar in Week 16.

Student workload

+ Contact hours
- 1 time 2h course introduction
- 12 times 3h theory, practice, and independent work: 12 x 3h = 36hours
+ Home work: approximately 60 hours
+ Seminar: approximately 34 hours

Total: approximately 130 hours

Evaluation methods and criteria

You can achieve maximum 100 points from individual assignments (homework or classwork) and the teamwork final seminar:
- 70 points from individual assignments (Part 1)
- 30 points from the teamwork assignment or final seminar (Part 2)

Students must achieve at least 50% of the individual assignments points (35 points) AND 50% (15 points )of the teamwork assignment/final seminar points to pass the course.

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

Failed (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.

0-49 points -> Fail

Assessment criteria, satisfactory (1-2)

50-59 points -> 1
60-69 points -> 2

Assessment criteria, good (3-4)

70-79 points -> 3
80-89 points -> 4

Assessment criteria, excellent (5)

90-100 points -> 5

Further information

+ 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