Course Syllabus

Artificial Intelligence  DRAFT

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Course Description

Artificial Intelligence (AI) is behind your smart phone’s intelligent personal assistant, driverless cars, robots, government fraud detection systems, and image recognition algorithms. This course introduces students to core techniques and applications of AI using primarily symbolic methods in an agent-oriented paradigm. Classes are a mix of discussions of theory of core concepts and hands-on problem-solving exercises. Course activities rely heavily on group work.

During the course, students implement simple search-based agents solving transportation tasks in a virtual environment. The virtual environment is an idealized model of systems of delivery, for example with robots in hospitals, or warehouse robots at Amazon. This part of the course is referred to as the programming project.

The course consists of the following components:

1. Introduction to AI (foundations of AI + intelligent agents)
2. The philosophy, ethics and future of AI
3. Problem-solving by searching: uninformed and informed search
4. Lab work on the programming project
5. Machine learning, neural networks and/or reinforcement learning. Only briefly covered. Covered in much more detail in the Machine Learning course.
6. Game playing and adversarial search
7. Lab work on AI in board games

Themes covered during the course:

1. Foundations of AI
2. Intelligent agents
3. Problem-solving by searching
4. Adversarial search (games)
5. Neural networks and/or reinforcement learning
6. Applications of AI
7. Philosophy, ethics and future of AI

Learning Objectives

Upon successfully completing this course, students will be able to:

• Correctly determine which AI technique(s) should be used to solve a particular problem - if any
• Design software agents that act rationally in complex domains
• Design formal problems in AI and identify important features and properties
• Explain concisely the scope of AI, its potential for society as well as its limitations
• Discuss contemporary applications of AI from both a technical and an ethical perspective

Faculty

TBA

Readings

Stuart Russell and Peter Norvig: Artificial Intelligence - A Modern Approach. 

All readings in the Course Summary refer to this textbook unless otherwise noted. 

Field Studies (may include):

• Board game playing
• Visit to the Digitalization Platform at the Royal Institute of Technology (KTH)
• Visit to IBM including exercises on IBM chatbot technology
• Visit to a Machine Learning startup

Expectations of the Students      

• Students should participate during sessions, discussions, group work and exercises.
• Students need to be present, arrive on time to all activities, and participate to receive full credit. The final grade will be affected by unexcused absences and lack of participation. The participation grade will be reduced by 10 points (over 100) for every unexcused absence. Remember to be in class on time!
• Classroom etiquette includes being respectful of other opinions, listening to others and entering a dialogue in a constructive manner.
• Students are expected to ask relevant questions in regards to the material covered.

Evaluation and Grading

More details will be provided by your instructor in class.

Assignments and presentation are conducted as group work.

General note regarding assignments

Papers should be correctly formatted and referenced. Double-spaced. Times New Roman. 12-point font. 1-inch margins. At DIS, one page equals 300 words. Late assignments will be deducted a third of a grade point per day it is late. All work must be handed in order to get a passing grade.