The Basics of AI
The course aims to provide an introduction and overview of artificial intelligence. The focus is on understanding the concept and some important techniques such as how search and machine learning work, as well as the consequences of AI on society.

Start reading whenever you want
You can start taking the course pretty much whenever you want since the course is an online course with flexible enrollment. You apply for the term in which you intend to start the course. If you want to start immediately, you apply for the current term, or you choose the term in which you plan to start. You select the term above, which will take you to the correct application occasion.

Course format
The course is a distance course that is completed at your own pace and is fully managed in a web-based course environment (https://www.owlhowl.se/). The course is based on self-study of the course material and is examined through self-correcting tests and submissions. If you have completed Elements of AI, you can enroll in this course to have your results validated. This applies to both the Swedish and the English version of the course. You do not have to redo the course; however, you must upload the certificate from Elements of AI and take a validation test with questions corresponding to those in Elements of AI to ensure that it is indeed you who has completed the course. For more information, see https://www.owlhowl.se/.

The course is supervised over the internet.

Learning objectives
The course's goal is to introduce concepts and applications within artificial intelligence (AI). After the course, the student will be able to:

- Understand and explain a number of fundamental problems, techniques, and concepts within artificial intelligence.

Course content
The course consists of six parts:

1. What is AI?

Definitions of AI Autonomy and adaptability Philosophical problems related to AI such as the Turing test and the Chinese room

2. Solving problems with AI

Formulating simple games like tic-tac-toe as a game tree Using the minimax principle to find optimal moves in a finite game

3. AI in practice

Expressing probabilities in terms of natural frequencies Bayes' rule for calculating risks

4. Machine learning

Why use machine learning? Unsupervised and supervised learning Learning methods such as the nearest neighbor method, linear regression, and logistic regression

5. Neural networks

What is a neural network and where are they used? The techniques behind neural networks

6. Consequences

Major consequences of AI on society such as AI-generated content, privacy, and work The difficulties of predicting the future and how to evaluate claims about AI.



For other questions, contact: Mattias.Sjostrand@Owlhowl.se