Study Guide

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:

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 8-10 lectures on the course topics using slides, videos, and demonstrations. In addition, 2-4 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.

• Teamwork Project: Students will work in their groups to design and implement an AI system for a real-world problem and present their work at the end of the semester in a 15-minutes presentation time.

No exam.

International connections

The course includes:
• Approximately 12 theory and guided exercises sessions where students work with practical tasks in a 2-hour - session (one hour with and one hour without the instructor).
• Some personal practice homework tasks.
• Group work seminar.
• Group work project.
• Students will get acquainted with the applications of AI in the real world via TUAS R&D groups

The group works are done in groups of 2-4 people outside of guidance sessions. The group sets aside 15 minutes outside of guidance sessions to present the group work.

Student workload

• Contact hours
- 12 times 3h theory and practice: 12 x 3h = 36hours
• Home work: approximately 69 hours
• Seminar: approximately 10 hours
• Group work project: approximately 15 hours

Total: approximately 130 hours

Content scheduling

Course Topics and Scheduling (pre-planning):
week 36: Introduction to AI: history, definitions, and applications.
week 37: Problem-solving methods in AI and Intelligent Agents
week 38: State Space Search Algorithms
week 39: Exercise Demonstrations I
week 40: Basics of Machine Learning
week 41: Data preprocessing for ML algorithms
week 43: Supervised Learning: training and evaluation of some supervised ML models like linear regression and logistic regression.
week 44: Ethical and Social Implications of AI + Exercise Demonstrations II
week 45-47: Applications of AI in Real-World Problems
week 48: Seminars presentations in groups of 2-4
week 49: Teamwork projects presentations in group of 2-4

Evaluation scale

H-5

Assessment methods and criteria

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

• In order to receive an approved performance, the student must receive an acceptable grade for 1) personal practice tasks, 2) group work seminar and 3) group work project.

• You can get at least 10 points for each personal practice task. You can therefore get a maximum of 60 points from all 6 personal practice tasks.

• Teamwork seminar: 15 points.

• Teamwork project: 25 points.

(The first acceptable points in personal exercises is 30, in seminar is 5, and in project is 10)

Assessment criteria, fail (0)

Less than 30 points in personal exercises, less than 5 points in groupwork seminar, and less than 10 points in groupwork project.

Assessment criteria, satisfactory (1-2)

30-39 points in personal exercises, 5-8 points in groupwork seminar, and 10-15 in groupwork project.

Assessment criteria, good (3-4)

40 - 49 points in personal exercises, 9-12 points in groupwork seminar, and 16-20 points in groupwork project.

Assessment criteria, excellent (5)

50 - 60 points in personal exercises, 13-15 points in groupwork seminar, and 21-25 points in groupwork project.