Basics of Artificial Intelligence (5 cr)
Code: TP00BN39-3002
General information
- Enrollment
-
08.02.2021 - 14.02.2021
Registration for the implementation has ended.
- Timing
-
01.03.2021 - 31.05.2021
Implementation has ended.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 1 cr
- Virtual proportion
- 4 cr
- Mode of delivery
- Blended learning
- Campus
- Laurea Leppävaara
- Teaching languages
- English
- Seats
- 20 - 40
- Degree programmes
- Laurea täydentävä osaaminen, amk-tutkinto (TON2), Tietojenkäsittely ja tietoliikenne (ICT)
- Complementary competence, bachelor's studies in English (CCN2), Information and Communication Technologies (ICT)
Learning outcomes
The student is able to:
- understand what is AI and how it can affect business
- recognize opportunities of AI in different domains
- analyze and visualize data
- knows the basic statistical methods used in data analysis
- knows how to use software to perform data analysis
- knows how to apply some basic methods used in AI
- knows trends in AI
- can recognize ethical challenges related to applying AI in business
Location and time
Online
Materials
AI for Dummies
ProQuest Ebook Central - Detail page
Azure Machine Learning Studio (Data Science) - Documentation
MS Power Apps Chatbot documentation
Other materials TBA
Teaching methods
Lectures and workshops
Independent study and teamwork
NOTE: Prerequisites: moderate knowledge of statistical and mathematical methods
Course Content
• definition of AI and basic concepts related to it
• business cases where AI is used
• methods and software for data analysis and visualization
• application of AI methods (Team work)
• recent trends in AI
• ethical issues in AI
Exam schedules
4.3
Introduction & Orientation
Lecture : What is AI
Intro: Exam (Individual or Group, Assignment 1)
Compulsory
11.3 No-workshop - Self Study
18.3 Exam
25.3
Power BI - Visualization & Data Science
- Workshop
- Individual Assignment 1 intro
1.4 No-workshop - Self Study
8.4
Basics of Data Science
- Lecture
- Workshop : Azure Machine Learning Studio
- Individual assignments 2 intro
15.4 No-workshop - Self Study
22.4 Workshop: Group work - Intro
29.4 No-workshop - Self Study
6.5 Workshop: Group work - Q & A
13.5 No-workshop - Self Study
Ascension Day
20.5 Team's Presesentation
Content scheduling
4.3
Introduction & Orientation
Lecture : What is AI
Intro: Exam (Individual or Group, Assignment 1)
Compulsory
11.3 No-workshop - Self Study
18.3 Exam
25.3
Power BI - Visualization & Data Science
- Workshop
- Individual Assignment 1 intro
1.4 No-workshop - Self Study
8.4
Basics of Data Science
- Lecture
- Workshop : Azure Machine Learning Studio
- Individual assignments 2 intro
15.4 No-workshop - Self Study
22.4 Workshop: Group work - Intro
29.4 No-workshop - Self Study
6.5 Workshop: Group work - Q & A
13.5 No-workshop - Self Study
Ascension Day
20.5 Team's Presesentation
Evaluation scale
Approved/Failed
Further information
Prerequisites: moderate knowledge of statistical and mathematical methods