Opetussuunnitelmat

Objective

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

Content

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

Location and time

Contact instruction at 3 or 4 weekends: on Fridays at 2pm - 8pm, and on Saturdays at 9am - 3pm.
Location: IT-Dynamo, Piippukatu 2, 40100 Jyväskylä.

Oppimateriaali ja suositeltava kirjallisuus

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).

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).

Further defined during the first contact session.

Teaching methods

Contact instruction at 3 or 4 weekends during the semester.
Exercises and distance learning between the contact sessions.
Study project covering a topic based on individual needs.

Employer connections

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.

Student workload

5 ECTS credits equals about 135 hours of study work.
Contact instruction: 36 hours
Exercises: 36 hours
Study project: 63 hours

Further information

Feedback on exercises and study project.
Self evaluation.
Peer evaluation.

Open UAS 5

Evaluation scale

0-5

Arviointikriteerit, tyydyttävä (1-2)

**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.

Arviointikriteerit, hyvä (3-4)

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, excellent (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.