Artificial IntelligenceLaajuus (5 cr)
Code: YTIP2100
Credits
5 op
Teaching language
- English
Responsible person
- Tuomo Sipola
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
Assessment criteria, satisfactory (1)
**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, good (3)
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.
Enrollment
01.08.2023 - 08.09.2023
Timing
28.08.2023 - 19.12.2023
Number of ECTS credits allocated
5 op
Virtual portion
4 op
Mode of delivery
20 % Face-to-face, 80 % Online learning
Unit
School of Technology
Teaching languages
- English
Seats
20 - 35
Degree programmes
- Master's Degree Programme in Artificial Intelligence and Data Analytics
Teachers
- Juha Peltomäki
Groups
-
YTI23S1Master's Degree Programme in Artificial Intelligence and Data-analytics
Objectives
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
Time and location
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.
Learning materials and recommended literature
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)
Practical training and working life connections
The aim is to connect the content of the course to problems that occur in working life.
Alternative completion methods
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 for students
The evaluation is based on a set of the following assignment types:
- learning project in a group
- assignments
Evaluation scale
0-5
Evaluation criteria, satisfactory (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.
Evaluation criteria, good (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.
Evaluation 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.
Enrollment
01.08.2022 - 04.09.2022
Timing
26.08.2022 - 16.12.2022
Number of ECTS credits allocated
5 op
Virtual portion
4 op
Mode of delivery
20 % Face-to-face, 80 % Online learning
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- English
Seats
0 - 35
Degree programmes
- Master's Degree Programme in Artificial Intelligence and Data Analytics
Teachers
- Juha Peltomäki
Groups
-
YTI22S1Master's Degree Programme in Artificial Intelligence and Data-analytics
Objectives
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
Time and location
Contact lectures during two weekends: on Fridays at 2pm - 8pm, and on Saturdays at 9am - 3pm.
Lectures are given at the following location: IT-Dynamo, Piippukatu 2, 40100 Jyväskylä.
Learning materials and recommended literature
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)
Practical training and working life connections
The aim is to connect the content of the course to problems that occur in working life.
Alternative completion methods
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 for students
The evaluation is based on a set of the following assignment types:
- learning project in a group
- assignments
Evaluation scale
0-5
Evaluation criteria, satisfactory (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.
Evaluation criteria, good (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.
Evaluation 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.