Elements of AI, 2 credits

Grunderna i AI, 2 hp

ETE318

Course starting semester

Main field of study

Computer Science

Course level

First cycle

Course type

Single subject course

Examiner

Fredrik Heintz

Director of studies or equivalent

Peter Dalenius

Education components

Preliminary scheduled hours: 0 h
Recommended self-study hours: 53 h

Contact

Possible deviation from course syllabus/programme syllabus

Course description

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.

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.

ECV = Elective / Compulsory / Voluntary

Course offered for		Semester	Period	Timetable module	Language	Campus	ECV
Single subject course (One-tenth-time, Mixed-time)		Autumn 2019	1, 2	-, -	Swedish	Distance
Single subject course (One-tenth-time, Mixed-time)		Autumn 2019	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

UPG1

Assignments

2 credits

U, G

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

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Peter Dalenius

Examiner

Fredrik Heintz

Course website and other links

Education components

Preliminary scheduled hours: 0 h
Recommended self-study hours: 53 h

Code	Name	Scope	Grading scale
UPG1	Assignments	2 credits	U, G

Student rights and obligations (login required)
Linköping University common rules and regulations (in Swedish)

There is no course literature available for this course in studieinfo.

Note: The course matrix might contain more information in Swedish.

I = Introduce, U = Teach, A = Utilize

		I	U	A	Modules	Comment
1. DISCIPLINARY KNOWLEDGE AND REASONING
	1.1 Knowledge of underlying mathematics and science (courses on G1X-level)
	1.2 Fundamental engineering knowledge (courses on G1X-level)
	1.3 Further knowledge, methods and tools in any of : mathematics, natural sciences, engineering (courses at G2X level)
	1.4 Advanced knowledge, methods and tools in any of: mathematics, natural sciences, engineering (courses at A1X level)
	1.5 Insight into current research and development work
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
	2.1 Analytical reasoning and problem solving
	2.2 Experimentation, investigation, and knowledge discovery
	2.3 System thinking
	2.4 Attitudes, thought, and learning
	2.5 Ethics, equity, and other responsibilities
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
	3.1 Teamwork
	3.2 Communications
	3.3 Communication in foreign languages
4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT
	4.1 Societal conditions, including economically, socially and ecologically sustainable development
	4.2 Enterprise and business context
	4.3 Conceiving, system engineering and management
	4.4 Designing
	4.5 Implementing
	4.6 Operating
5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS
	5.1 Societal conditions, including economically, socially and ecologically sustainable development within research or development projects
	5.2 Economic conditions for research or development projects
	5.3 Identification of needs, structuring and planning of research or development projects
	5.4 Execution of research or development projects
	5.5 Presentation and evaluation of research or development projects

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.