Elements of AI: Introduction to AI

No formal prerequisites beyond high-school mathematics (basic arithmetics with fractions)

After completing the course, you will be able to:

• Identify autonomy and adaptivity as key concepts of AI
• Distinguish between realistic and unrealistic AI (science fiction vs. real life)
• Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment
• Formulate a real-world problem as a search problem
• Formulate a simple game (such as tic-tac-toe) as a game tree
• Use the minimax principle to find optimal moves in a limited-size game tree
• Express probabilities in terms of natural frequencies
• Apply the Bayes rule to infer risks in simple scenarios
• Explain the base-rate fallacy and avoid it by applying Bayesian reasoning
• Explain why machine learning techniques are used
• Distinguish between unsupervised and supervised machine learning scenarios
• Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression
• Explain what a neural network is and where they are being successfully used
• Understand the technical methods that underpin neural networks
• Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
• Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work

The course material is available at https://www.elementsofai.com/(Opens in a new tab)

Optional course in the Bachelor Programme of Computer Science

• suitable for all students in any study programme
• the target audience is especially students with little or no computer science studies
• The course may be offered among the university’s continuous learning courses.
  

• any stage of studies
• the course is offered continuously
• the course can be started at any time, and completed at any pace
• recommended duration is six weeks

• Exercises include multiple choice quizzes, numerical exercises, and questions that require a written answer. 
• The multiple choice and numerical exercises are automatically checked. 
• The exercises with written answers are reviewed by other students (peer grading) and in some cases by the instructors.
• The course material contains text and interactive elements.
• The exercises are designed to challenge you to re-read the material and access further sources enough to be able to produce an answer. 

Open University and degree programme completion methods may be different.

• Successful completion requires 90% completed exercises and minimum 50% correctness
• Only one attempt is allowed in the multiple choice quizzes and numerical exercises
• Exercises with written answers are accepted or rejected based on the reviews: in case of rejection, another attempt is allowed (as many times as required)

After the course, if the student wishes to continue learning about AI, we recommend learning some programming and taking introductory AI courses. These courses are mainly organized by the faculty:

• the follow-up course Elements of AI: Building AI. 
• DATA15001 Introduction to AI is a closely related intermediate course that also includes programming exercises on the same topics
• DATA11001 Introduction to Data Science (advanced course)
• DATA11002 Introduction to Machine Learning (advanced course)
• closely related Bachelor programmes include the Bachelor of Science and Bachelor of Computer Science
• closely related Master's programmes include the Master of Data Science and Master of Computer Science