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Basics of Artificial Intelligence (5 cr)

Code: TP00BN39-3002

General information

Enrollment

08.02.2021 - 14.02.2021

Timing

01.03.2021 - 31.05.2021

Number of ECTS credits allocated

5 op

Virtual proportion

4 op

Mode of delivery

20 % Contact teaching, 80 % Distance learning

Unit

Laurea Leppävaara, tiko

Campus

Laurea Leppävaara

Teaching languages

  • English

Seats

20 - 40

Degree programmes

  • Complementary competence, bachelor's studies in English (CCN2), Information and Communication Technologies (ICT)
  • Laurea täydentävä osaaminen, amk-tutkinto (TON2), Tietojenkäsittely ja tietoliikenne (ICT)

Teachers

  • Jouni Takala
  • Lassi Tissari

Teacher in charge

Lassi Tissari

Groups

  • CCN220SY
    Complementary competence (bachelor’s studies), S20, Information and Communication Technologies (ICT)
  • CCH220SY
    Complementary competence (bachelor’s studies), S20, Business, administration and law
  • TOH220SY
  • TON220SY

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%