COURSE SYLLABUS

Introduction to Artificial Intelligence 7.5 credits

Introduktion till artificiell intelligens
First cycle, D0030E
Version
Course syllabus valid: Spring 2021 Sp 3 - Present
The version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first.

Syllabus established
by Jonny Johansson, HUL SRT 18 Jun 2020

Last revised
by Jonny Johansson, HUL SRT 18 Jun 2020

Education level
First cycle
Grade scale
U G#
Subject
Computer Science and Engineering
Subject group (SCB)
Computer Technology

Entry requirements

In order to meet the general entry requirements for first cycle studies you must have successfully completed upper secondary education and documented skills in English language + Swedish upper secondary school courses Mathematics 3b/3c (specifik entry A4). Or: Swedish upper secondary school courses English B, Mathematics C (specifik entry 4)


More information about English language requirements


Selection

The selection is based on final school grades or Swedish Scholastic Aptitude Test.



Course Aim

Upon course completion, the student should have the ability to:
 
  • Describe the most common AI methods, particularly in Reasoning, Machine Learning, and Robotics .
  • List existing tools which implement AI methods.
  • Categorize a given real-world problem using standard concepts for defining the problem situation.
  • Choose appropriate AI methods for a given real-world problem.
  • Discuss ethical and sustainability issues related to AI.

Contents

The topics covered are: introduction to AI, introduction to neuroscience concepts, fuzzy logic, regression (linear, logistic), classification (K-NN), dimensionality reduction (PCA), support vector machines (SVM), clustering (K-means), decision trees, Bayesian learning, neural networks, deep neural networks (CNN, RNN and LSTM), data cleaning, models’ evaluation, features selection, AI ethics and governance.


Realization

Lectures will be given online and there will be available also as recorder after the completion of each lecture. Course assignments in the form of multiple-choise questions or code completion will be given after each unit (collection of lectures) is complete. Before and after the assignments are solved, there will be lectures to present and discuss different solutions. At the end of the course, a panel discussion will be organized where invited speakers from the AI field and participants will discuss about the current progress and trends of AI, its applications and the future of AI.

Participants are expected to:

  • have internet connection (minimum 0,5 Mbps), microphone, Web cam
  • use their personal computers during the course. The participants need to guarantee they have all administration rights on their machines in order to install and use the necessary tools during the course.

Examination

Web based course assignments to assess student abilities in understanding AI algorithms and how these can be applied to solve real life problems. The assignments will be given through out the course in order to evaluate student’s course understanding and feedback will be given for the continuation of the course.


Examiner
Marcus Liwicki

Literature. Valid from Spring 2020 Sp 3 (May change until 10 weeks before course start)
Title: Artificial Intelligence : A Moderna Approach
Author: Stuart J. Russel and Peter Norvig

Elements of AI (https://www.elementsofai.com/)

Course offered by
Department of Computer Science, Electrical and Space Engineering

Modules
CodeDescriptionGrade scaleHPStatusFrom periodTitle
0001Compulsory assignmentsU G#7.50MandatoryS20

Study guidance
Study guidance for the course is to be found in our learning platform Canvas before the course starts. Students applying for single subject courses get more information in the Welcome letter. You will find the learning platform via My LTU.