Basics of Artificial Intelligence (5 cr)
Code: TP00BN39-3004
General information
Enrollment
06.02.2023 - 12.02.2023
Timing
03.03.2023 - 31.07.2023
Number of ECTS credits allocated
5 op
Virtual proportion
5 op
RDI proportion
4 op
Mode of delivery
Distance learning
Unit
Laurea Leppävaara, tiko
Campus
Laurea Virtual Campus
Teaching languages
- English
Seats
20 - 40
Degree programmes
- Laurea täydentävä osaaminen, amk-tutkinto (TOH2), Kauppa, hallinto ja oikeustieteet
Teachers
- Jouni Takala
- Saifuddin Saif
Teacher in charge
Jouni Takala
Groups
-
CCH222SYComplementary competence (bachelor’s studies), S22, Business, administration and law
-
TOH222SYTäydentävä osaaminen (amk-tutkinto), S22, Kauppa, hallinto ja oikeustieteet
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
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
Location and time
Online
Learning materials and recommended literature
AI for Dummies
ProQuest Ebook Central - Detail page
Azure Machine Learning Studio (Data Science) - Documentation
MS Power Apps Chatbot documentation
Other materials TBA
Important dates
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 and 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
Further information for students
Prerequisites: moderate knowledge of statistical and mathematical methods
Grading scale
Approved/Failed
Evaluation methods and criteria
Individual assignments (Exam and assignments) 30%
Project work in team 70%