•   Basics of Artificial Intelligence TP00BN39-3002 04.03.2021-31.05.2021  5 credits  (CCN220SY, ...) +-
    Learning outcomes of the course
    The student is able to:
    • understand what is AI and how it can affect business
    • recognize opportunities of AI in different domains
    • is able to 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
    Teacher in charge

    Lassi Tissari

    Mode of delivery

    20% Face-to-face, 80% Distance learning

    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

    Teaching methods

    Lectures and workshops
    Independent study and teamwork
    NOTE: Prerequisites: basics of statistical research and statistical 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

    Evaluation methods and criteria

    Individual assignments (Exam and assignments) 30%
    Project work in team 70%

    Language of instruction
    • English
    Completion language(s)
    • English
    Timing

    04.03.2021 - 31.05.2021

    Enrollment period

    08.02.2021 - 14.02.2021

    Group(s)
    • CCN220SY
    • CCH220SY
    • TOH220SY
    • TON220SY
    Seats

    20 - 40

    Unit

    Laurea Leppävaara, tiko

    Teacher(s)

    Jouni Takala, Lassi Tissari

    Further information for students

    Prerequisites: basics of statistical research and statistical methods

    Programme(s)

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

    Campus

    Laurea Leppävaara

    Virtual portion

    4 credits

    Evaluation scale

    Approved/Failed

    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

    Location and time

    Online