Opinto-opas

Tavoitteet

After completing the course, the students can
- work in the AI project
- describe and understand how AI projects are implemented

Sisältö

Practical project related to AI

Oppimateriaalit

The course materials will be announced later during the course.

Tenttien ajankohdat ja uusintamahdollisuudet

No Exam

Opiskelijan ajankäyttö ja kuormitus

Contact hours:
- 4 times 4h theory and practice: 4 x 4h = 16 hours

Final project: approximately 114 hours

Total: approximately: 130 hours

Sisällön jaksotus

A deep learning-focused, project-based course that empowers master's students to explore and develop AI projects, fostering their ability to conceive, implement, and optimize intelligent solutions.

Session 1: AI Project Lifecycle and Data Selection
Session 2: Data Preprocessing and Model Selection
Session 3: Presenting First Results and Discussion
Session 4: Algorithm Optimization and Tuning

Arviointiasteikko

Hyväksytty/Hylätty

Tavoitteet

After completing the course, the students can
- work in the Data Engineering project
- describe and understand how Data Engineering projects are implemented

Sisältö

Practical project related to Data Engineering

Oppimateriaalit

Teacher provided lecture material
Supporting public online material
Teacher provided virtual machines
All needed material (or at least a link to them) will be available in itslearning.

Opetusmenetelmät

Contact learning, practical exercises, independent study

Kansainvälisyys

Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.

Opiskelijan ajankäyttö ja kuormitus

Contact hours 16 h
Inpendent studying 119h, including:
- Studying the course material
- Completing assignments
- Project

Sisällön jaksotus

-The basic idea of data engineering methods and pipelines
-different components
-integration of said components (MQ systems)
-data engineering frameworks (Apache family)

Viestintäkanava ja lisätietoja

Itslearning and contact classes are the main communication channels used on this course.

The student is required to have a computer capable of running a simple Ubuntu virtual machine.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

Assignments returned throughout the course
Small project at the end of the course

Tavoitteet

After completing the course, the student can:
- learn about the history and background of Cloud Computing
- discover Cloud Computing including examples of real-world problems
- understand the most commonly used platforms for cloud computing such as Amazon AWS and Microsoft Azure (and possibly CSC Finland).
- understand the various Service models on Cloud as IaaS, PaaS, and SaaS
- learn the basic network security techniques in the cloud environment

Sisältö

- Cloud computing
- Software-as-a-Service (SaaS)
- Platform-as-a-Service (PaaS)
- Infrastructure-as-a-Service (IaaS)
- Cloud computing technologies and tools
- Security in the cloud environment

Arviointiasteikko

H-5

Tavoitteet

After completing the course, the student can:
- describe basic concepts and processes related to Data Engineering and AI

Sisältö

- Data Engineering process
- Basics of AI
- Fields and evolution of AI
- Big data
- Basics of Machine Learning

Oppimateriaalit

Material will be available via the learning environment (ITS) and our institution's eBook Central database.

Tenttien ajankohdat ja uusintamahdollisuudet

No exam

Opiskelijan ajankäyttö ja kuormitus

Contact hours:
- 4 times 3h theory and practice: 4 x 4h = 12 hours

Assignments for the final project: approximately 118 hours

Total: approximately: 130 hours

Sisällön jaksotus

Course Outline and Schedule:
Week 37: Introduction to the Course and Data Preprocessing:
• Project overview and requisites
• Initiation of project's initial phase: Data preprocessing and visualization (assignment 1)
Week 41: Machine Learning I
• Students' presentations showcasing outputs and implementation of the first assignment
• Commencement of phase two: Supervised and unsupervised learning (assignment 2)
Week 45: Machine Learning II
• Students' presentations showcasing outputs and implementation of the second assignment
• Tuning and optimizing your Machine Learning algorithms (assignment 3)

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

The course is graded on a scale of 0-5.
*
In order to receive an approved performance and pass the course, the student must receive an acceptable mark for the three assignments.
*
You can get at most 40 points for each phase of the project (assignments 1-3). You can therefore get a maximum of 120 points from all phases of the project.

Hylätty (0)

Less than 60 points in assignments not passed.

Arviointikriteerit, tyydyttävä (1-2)

60 - 79 points from the total points of the assignments

Arviointikriteerit, hyvä (3-4)

80 - 99 points from the total points of the assignments

Arviointikriteerit, kiitettävä (5)

100 - 120 points from the total points of the assignments

Tavoitteet

After completing the course, the students can
- describe and understand how MLOps projects operate

Sisältö

- Process and tools for Machine Learning Engineering for Production (MLOps)

Oppimateriaalit

Teacher provided lecture material
Supporting public online material
Teacher provided virtual machines
All needed material (or at least a link to them) will be available in itslearning.

Opetusmenetelmät

Contact learning, practical assignments, independent study

Kansainvälisyys

Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.

Opiskelijan ajankäyttö ja kuormitus

Contact hours 16 h
Inpendent studying 119h, including:
- Studying the course material
- Completing assignments
- Project

Sisällön jaksotus

Chosen topics from MLops, Dataops and Devops for artificial intelligence and data engineering students.

Viestintäkanava ja lisätietoja

Itslearning and contact classes are the main communication channels used on this course.

The student is required to have a computer capable of running a simple Ubuntu virtual machine.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

Assignments returned throughout the course
Small project at the end of the course

Tavoitteet

After completing the course, the students can
- work in the AI project
- describe and understand how AI projects are implemented

Sisältö

Practical project related to AI

Oppimateriaalit

The course materials will be announced later during the course.

Tenttien ajankohdat ja uusintamahdollisuudet

No Exam

Opiskelijan ajankäyttö ja kuormitus

Contact hours:
- 4 times 4h theory and practice: 4 x 4h = 16 hours

Final project: approximately 114 hours

Total: approximately: 130 hours

Sisällön jaksotus

A deep learning-focused, project-based course that empowers master's students to explore and develop AI projects, fostering their ability to conceive, implement, and optimize intelligent solutions.

Session 1: AI Project Lifecycle and Data Selection
Session 2: Data Preprocessing and Model Selection
Session 3: Presenting First Results and Discussion
Session 4: Algorithm Optimization and Tuning

Arviointiasteikko

Hyväksytty/Hylätty

Tavoitteet

After completing the course, the students can
- describe and understand how MLOps projects operate

Sisältö

- Process and tools for Machine Learning Engineering for Production (MLOps)

Oppimateriaalit

Teacher provided lecture material
Supporting public online material
Teacher provided virtual machines
All needed material (or at least a link to them) will be available in itslearning.

Opetusmenetelmät

Contact learning, practical assignments, independent study

Kansainvälisyys

Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.

Opiskelijan ajankäyttö ja kuormitus

Contact hours 16 h
Inpendent studying 119h, including:
- Studying the course material
- Completing assignments
- Project

Sisällön jaksotus

Chosen topics from MLops, Dataops and Devops for artificial intelligence and data engineering students.

Viestintäkanava ja lisätietoja

Itslearning and contact classes are the main communication channels used on this course.

The student is required to have a computer capable of running a simple Ubuntu virtual machine.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

Assignments returned throughout the course
Small project at the end of the course

Tavoitteet

After completing the course, the students can
- work in the Data Engineering project
- describe and understand how Data Engineering projects are implemented

Sisältö

Practical project related to Data Engineering

Oppimateriaalit

Teacher provided lecture material
Supporting public online material
Teacher provided virtual machines
All needed material (or at least a link to them) will be available in itslearning.

Opetusmenetelmät

Contact learning, practical exercises, independent study

Kansainvälisyys

Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.

Opiskelijan ajankäyttö ja kuormitus

Contact hours 16 h
Inpendent studying 119h, including:
- Studying the course material
- Completing assignments
- Project

Sisällön jaksotus

-The basic idea of data engineering methods and pipelines
-different components
-integration of said components (MQ systems)
-data engineering frameworks (Apache family)

Viestintäkanava ja lisätietoja

Itslearning and contact classes are the main communication channels used on this course.

The student is required to have a computer capable of running a simple Ubuntu virtual machine.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

Assignments returned throughout the course
Small project at the end of the course

Tavoitteet

After completing the course, the student can:
- learn about the history and background of Cloud Computing
- discover Cloud Computing including examples of real-world problems
- understand the most commonly used platforms for cloud computing such as Amazon AWS and Microsoft Azure (and possibly CSC Finland).
- understand the various Service models on Cloud as IaaS, PaaS, and SaaS
- learn the basic network security techniques in the cloud environment

Sisältö

- Cloud computing
- Software-as-a-Service (SaaS)
- Platform-as-a-Service (PaaS)
- Infrastructure-as-a-Service (IaaS)
- Cloud computing technologies and tools
- Security in the cloud environment

Arviointiasteikko

H-5

Tavoitteet

After completing the course, the student can:
- describe basic concepts and processes related to Data Engineering and AI

Sisältö

- Data Engineering process
- Basics of AI
- Fields and evolution of AI
- Big data
- Basics of Machine Learning

Oppimateriaalit

Material will be available via the learning environment (ITS) and our institution's eBook Central database.

Tenttien ajankohdat ja uusintamahdollisuudet

No exam

Opiskelijan ajankäyttö ja kuormitus

Contact hours:
- 4 times 3h theory and practice: 4 x 4h = 12 hours

Assignments for the final project: approximately 118 hours

Total: approximately: 130 hours

Sisällön jaksotus

Course Outline and Schedule:
Week 37: Introduction to the Course and Data Preprocessing:
• Project overview and requisites
• Initiation of project's initial phase: Data preprocessing and visualization (assignment 1)
Week 41: Machine Learning I
• Students' presentations showcasing outputs and implementation of the first assignment
• Commencement of phase two: Supervised and unsupervised learning (assignment 2)
Week 45: Machine Learning II
• Students' presentations showcasing outputs and implementation of the second assignment
• Tuning and optimizing your Machine Learning algorithms (assignment 3)

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

The course is graded on a scale of 0-5.
*
In order to receive an approved performance and pass the course, the student must receive an acceptable mark for the three assignments.
*
You can get at most 40 points for each phase of the project (assignments 1-3). You can therefore get a maximum of 120 points from all phases of the project.

Hylätty (0)

Less than 60 points in assignments not passed.

Arviointikriteerit, tyydyttävä (1-2)

60 - 79 points from the total points of the assignments

Arviointikriteerit, hyvä (3-4)

80 - 99 points from the total points of the assignments

Arviointikriteerit, kiitettävä (5)

100 - 120 points from the total points of the assignments