• Artificial Intelligence (YTIP2100-3003),
         26.08.2022 – 16.12.2022,  5 cr  (YTI22S1) — Face-to-face, Online learning +-
    Learning outcomes of the course
    The student understands the purpose of artificial intelligence and knows the importance and possibilities of data. The student understands the importance of data quality and the ethics of artificial intelligence. The student will know the steps of data analytics/machine learning/deep learning processes. The student has knowledge and skills to plan a data strategy.

    Course competences
    EUYKN EUR-ACE: Knowledge and Understanding, Master's Degree

    EUYCT EUR-ACE: Communication and Team-working, Master's Degree

    EUYLL EUR-ACE: Lifelong Learning, Master's Degree
    Course contents
    The key topics of the course are:
    The possibilities of data
    Ethics of Artificial Intelligence (AI)
    The importance of data quality
    Methodology of Data analytics (DA) and AI
    DA and AI project process and management
    Data strategy
    Open source tools to DA, ML and AI
    Assessment criteria
    Assessment criteria - grade 1 and 2
    **Assessment criteria, sufficient 1, satisfactory 2
    Sufficient 1: Student has sufficient knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a partially comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors at a sufficient level.

    Satisfactory 2: Student has satisfactory knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a partially comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors at a satisfactory level.
    Assessment criteria - grade 3 and 4
    Good 3: Student has good knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a partially comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors well.

    Very Good 4: Student has very good knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors very well.
    Assessment criteria - grade 5
    Excellent 5: Student has excellent knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a comprehensive data strategy and describe the DA/AI process. In addition, student understands excellently the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors.
    Language of instruction

    English

    Location and time

    Contact instruction over 2 weekends: on Fridays at 2pm - 8pm, and on Saturdays at 9am - 3pm.
    Location: IT-Dynamo, Piippukatu 2, 40100 Jyväskylä.

    Planned learning activities, teaching methods and guidance

    Contact instruction over 2 weekends during the semester.
    Exercises and distance learning between the contact sessions.
    Study project covering a topic based on individual needs.

    Learning materials and recommended literature

    Further defined during the first contact session and more can be found in the slide sets.

    Relevant parts of the following books:

    The Essential AI Handbook for Leaders
    by Peltarion (59 pages).

    Ethical Artificial Intelligence
    by Bill Hibbard (2015, 177 pages).

    The Quest for Artificial Intelligence: A History of Ideas and Achievements
    by Nils J. Nilsson (2009, 707 pages).

    A Brief Introduction to Machine Learning for Engineers
    by O. Simeone (237 pages)

    The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition (corrected)
    by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2017, 764 pages).

    Python Data Science Handbook: Essential Tools for Working with Data
    by Jake VanderPlas (2016 O'Reilly Media, 541 pages).

    pandas: powerful Python data analysis toolkit
    by Wes McKinney and the Pandas Development Team (2020, 3071 pages).

    Lecturer(s)

    Tuomo Sipola

    Working life cooperation

    It is advisable that the students use their own work environment as an object of study and as a data source.
    Possible guest lecturers from companies.

    Campus

    Lutakko Campus

    Timing

    26.08.2022 - 16.12.2022

    Learning assignments and student workload

    5 ECTS credits equals about 135 hours of study work.
    Contact instruction: 24 hours
    Exercises: 48 hours
    Study project: 63 hours

    Enrollment

    01.08.2022 - 04.09.2022

    Groups
    • YTI22S1
    Seats

    0 - 35

    Assessment methods

    Feedback on exercises and study project.
    Self evaluation of study project.
    Peer evaluation of study project.
    Teacher's evaluation of study project.

    Open UAS 5

    Degree Programme

    Master's Degree Programme in Artificial Intelligence and Data Analytics

    Mode of delivery

    Face-to-face, Online learning

    Share of virtual studies

    4 cr

    Credits
    • 5 cr
    Unit

    School of Technology