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 lectures on campus:
* Sat 2.9.2023 from 9.00 to 15.00
* Sat 28.10.2023 from 9.00 to 15.00

Lectures are given at the following location: IT-Dynamo, Piippukatu 2, 40100 Jyväskylä.

Oppimateriaali ja suositeltava kirjallisuus

Learning material is defined during the first contact session and more can be found in the slide sets.

Relevant parts of the following books can be used as background material:

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).

Teaching methods

- contact lectures during two weekends per semester
- independent study
- distance learning
- assignments
- learning project (group work)

Employer connections

The aim is to connect the content of the course to problems that occur in working life.

Vaihtoehtoiset suoritustavat

The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.

Student workload

One credit (1 Cr) corresponds to an average of 27 hours of work. Five (5) ECTS credits equals about 135 hours of study work.

- lectures 24 h
- learning project and seminar 40 h
- assignments 40 h
- independent study 31 h

Total 135 h

Further information

The evaluation is based on a set of the following assignment types:

- learning project in a group
- assignments

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.