Elements of AI, 2 credits (ETE318)

Grunderna i AI, 2 hp

Course description

Course content

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

Start the course whenever you want

You can start the course almost anytime you want as the course is an online course with flexible admission. You make the application for the semester you intend to start reading the course. If you want to start directly, you apply for the current semester, or you choose the semester you intend to start. Choose the semester you are interested in above, and you will find the right application opportunity.

Course outline

Elements of AI is an online course that you can do at any time and in any pace at https://www.elementsofai.se/. The course is a self study course of the online material and is examined through self correcting and peer reviewed exercises. When you have completed Elements of AI (either the Swedish or the English version of the course) you can register to this course to get your results validated. When you have been accepted to this course, we will contact you with information about how to validate the results. To verify that you have really taken the online course, you may have to answer questions similar to the ones in the online course.

The course is given and examined over the Internet.

Information on general entry requirements 

Please note that you must be able to prove that you fulfill the general entry requirements when applying for the course. If your final school grades are not already on your pages at antagning.se, then you need to upload your upper secondary qualification, or equivalent, at antagning.se in connection with your application.

Main field of study

Computer Science

Level

First cycle

Course type

Single subject course

Examiner

Fredrik Heintz

Director of studies or equivalent

Peter Dalenius

Contact

Course offered for Semester Period Timetable module Language Campus VOF
Single subject course (One-tenth-time, Mixed-time) Spring 2021 1, 2 -, - Swedish Distance
Single subject course (One-tenth-time, Mixed-time) Spring 2021 1, 2 -, - English Distance

Main field of study

Computer Science

Course level

First cycle

Advancement level

G1X

Entry requirements

General entry requirements for undergraduate studies

Prerequisites

None

Intended learning outcomes

  • Distinguish between realistic and unrealistic AI (science fiction vs.
  • real life)
  • Express some basic philosophical problems related to AI
  • Formulate a simple real-world problem as a search problem
  • Apply the Bayes rule to infer risks in simple scenarios
  • Explain the base-rate fallacy and how to 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 some supervised classification methods
  • 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

Course content

The material is divided in six chapters which are:

1. What is AI?

  • Definitions of AI
  • Autonomy and adaptivity
  • Philosophical problems related to AI including the Turing test and the Chinese room thought experiment

2. AI problem solving

  • 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

3. Real world AI

  • Expressing probabilities in terms of natural frequencies
  • Bayes rule to infer risks in simple scenarios
  • The base-rate fallacy and how to avoid it by applying Bayesian reasoning

4. Machine learning

  • Why use machine learning
  • Unsupervised and supervised machine learning scenarios
  • Supervised classification methods: the nearest neighbor method, linear regression, and logistic regression

5. Neural networks

  • What is a neural network is and where are they being successfully used
  • The technical methods that underpin neural networks

6. Implications

  • Major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work
  • The difficulty of predicting the future and how to evaluate claims made about AI

Teaching and working methods

An open online course Elements of AI (https://course.elementsofai.se), consisting of text and interactive elements

Examination

UPG1AssignmentsU, G2 credits

Assessment is based on exercises, including multiple choice quizzes, numerical exercises, and questions that require a written answer. The multiple choice and numerical exercises are automatically checked, and the exercises with written answers are reviewed by other students (peer

grading) and in some cases by the instructors.Successful completion of the course requires at least 90% completed exercises and minimum 50% correctness. The course is graded as pass/fail (no numerical grades).

Grades

Two grade scale, older version, U, G

Other information

About teaching and examination language

The teaching language is presented in the Overview tab for each course. The examination language relates to the teaching language as follows: 

  • If teaching language is Swedish, the course as a whole or in large parts, is taught in Swedish. Please note that although teaching language is Swedish, parts of the course could be given in English. Examination language is Swedish. 
  • If teaching language is Swedish/English, the course as a whole will be taught in English if students without prior knowledge of the Swedish language participate. Examination language is Swedish or English (depending on teaching language). 
  • If teaching language is English, the course as a whole is taught in English. Examination language is English. 

Other

The course is conducted in a manner where both men's and women's experience and knowledge are made visible and developed. 

The planning and implementation of a course should correspond to the course syllabus. The course evaluation should therefore be conducted with the course syllabus as a starting point.  

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Peter Dalenius

Examiner

Fredrik Heintz

Course website and other links

Education components

Preliminär schemalagd tid: 0 h
Rekommenderad självstudietid: 53 h
There is no course literature available for this course.
UPG1 Assignments U, G 2 credits

Assessment is based on exercises, including multiple choice quizzes, numerical exercises, and questions that require a written answer. The multiple choice and numerical exercises are automatically checked, and the exercises with written answers are reviewed by other students (peer

grading) and in some cases by the instructors.Successful completion of the course requires at least 90% completed exercises and minimum 50% correctness. The course is graded as pass/fail (no numerical grades).

This tab contains public material from the course room in Lisam. The information published here is not legally binding, such material can be found under the other tabs on this page. There are no files available for this course.

Page responsible: Study information, bilda@uf.liu.se