Course Syllabus

Artificial Intelligence

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 on social media, just to mention a few examples.  This course introduces you to core techniques and applications of Artificial Intelligence using primarily symbolic search-based methods in an agent-oriented paradigm.

Classes are a mix of discussions of theory/core concepts and hands-on problem solving. The majority of the course work is carried out in groups.

During the course, you will implement simple search-based agents solving navigation and transportation tasks in a virtual environment. The virtual environment is an idealized model of systems of delivery robots in hospitals, the warehouse robots at Amazon, etc. This part of the course is referred to as the programming project.

The course consists of the following parts:

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 Neural Networks and Deep Learning course. 
6. Game playing and adversarial search. 
7. Lab work on AI in board games.

Course Themes

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

Learning Objectives

Upon successfully completing this course, you 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

Lorenzo Belgrano 

Mathematical Modelling and Computation, DTU, 2019, Machine Learning Engineer, Corti, 2019 - present

Readings

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

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

Field Studies (TBD, may Include):

• Board game playing
• Visit to the AI and robotics facilities at the Technical University of Denmark (DTU)
• Visit to IBM including exercises on IBM chatbot technology
• Visit to LEO Innovation Lab developing AI-based health apps
• Visit to Flow Robotics, a Danish startup making pipetting robots.  

Expectations of the Students      

You are expected to attend all DIS classes when scheduled. All classes, events and field trips are mandatory unless marked otherwise in the course plan. If you miss multiple classes, the Director of Academic Support and the Director of Student Affairs will be notified. Absences will jeopardize your grade and your standing at DIS.  Allowances will be made in cases of illness, but in the case of multiple absences, you will need to provide a doctor’s note.

Evaluation and Grading

More details will be provided by your instructor in class.

The assignments and the presentation are 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. Papers not adhering to these guidelines will result in point deductions. Late assignments will be deducted a third of a grade point per day it is late. All work must be handed in in order to get a passing grade.