Curricula

Objectives

Do You Want to learn how to understand cloud services that enable the construction of AI (Artificial Intelligence) and ML (machine learning) services with ready-made models and cloud services? In the course, the students will familiarise and utilise artificial intelligence and machine learning services with image- and optical character recognition and forecasting services in the cloud. The course enables students to understand the potential of cloud-based AI and machine learning services in a digital operating environments.

Course competencies
- Web development competence
- Systems and methods in ICT
- Learning and information management competence

Content

- Artificial intelligence and machine learning services in the cloud.
- document analysis
- forecasting
- image recognition
- other current/relevant AI and machine learning services

Learning materials and recommended literature

Material written by the teacher can be found at Moodle. Any AWS Academy Learning material or additional machine learning and artificial intelligence books can be used as additional material.

Teaching methods

- Lectures (in-class)
- Contact lessons
- Exercises
- Tests
- Final exam

The primary mode of delivery is a combination of in-class activities on campus. The in-class activities deepen the learning in the forms of tutorials, individual and group works, reflection and guidance.

Exam dates and retake possibilities

The final exam is held at the end of the course. Renewal opportunities will be announced at the end of the course.

Student workload

Approximately 135 hours.

Further information for students

Avoin amk 3
EduFutura 5

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Sufficient 1
You have basic knowledge in the area of AI & Machine learning services in the cloud. You know the components and terms of data preprocessing cloud services and what they mean.

Satisfactory 2
You have knowledge in the area of AI & Machine learning services in the cloud. You know the components terms of data preprocessing cloud services and what they mean.

Evaluation criteria, good (3-4)

Good 3
You understand the concepts of AI & Machine learning services in the cloud and can apply the gained knowledge in a real-life data preprocessing problems.

Very good 4
You understand the concepts of AI & Machine learning services in the cloud and can apply the gained knowledge in a real-life AI & Machine learning problems and use cases. You have expanded your knowledge independently during the course.

Evaluation criteria, excellent (5)

Excellent 5
In addition to previous requirements you can analytically discuss about the concepts in AI & Machine learning services in the cloud and present and defend (based on knowledge and evidence) your opinions. You can question the presented information and give valid options.

Prerequisites

-Web Development Tools
-SQL Basics
-Basics of programming
-Use of command prompt and/or terminal

Objectives

Do You Want to learn how to understand cloud services that enable the construction of AI (Artificial Intelligence) and ML (machine learning) services with ready-made models and cloud services? In the course, the students will familiarise and utilise artificial intelligence and machine learning services with image- and optical character recognition and forecasting services in the cloud. The course enables students to understand the potential of cloud-based AI and machine learning services in a digital operating environments.

Course competencies
- Web development competence
- Systems and methods in ICT
- Learning and information management competence

Content

- Artificial intelligence and machine learning services in the cloud.
- document analysis
- forecasting
- image recognition
- other current/relevant AI and machine learning services

Learning materials and recommended literature

Material written by the teacher can be found at Moodle. Any AWS Academy Learning material or additional machine learning and artificial intelligence books can be used as additional material.

Teaching methods

- Lectures (online or in-class)
- Contact lessons
- Exercises
- Tests
- Final exam

The primary mode of delivery is a combination of in-class activities on campus and on-line lectures. The lectures will be recorded to support opportunities for 24/7 learning. The in-class activities deepen the learning in the forms of tutorials, individual and group works, reflection and guidance. However, if the COVID-19 situation requires the learning will be supported by on-line tutorials.

Exam dates and retake possibilities

The final exam is held at the end of the course. Renewal opportunities will be announced at the end of the course.

Student workload

Approximately 135 hours.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Sufficient 1
You have basic knowledge in the area of AI & Machine learning services in the cloud. You know the components and terms of data preprocessing cloud services and what they mean.

Satisfactory 2
You have knowledge in the area of AI & Machine learning services in the cloud. You know the components terms of data preprocessing cloud services and what they mean.

Evaluation criteria, good (3-4)

Good 3
You understand the concepts of AI & Machine learning services in the cloud and can apply the gained knowledge in a real-life data preprocessing problems.

Very good 4
You understand the concepts of AI & Machine learning services in the cloud and can apply the gained knowledge in a real-life AI & Machine learning problems and use cases. You have expanded your knowledge independently during the course.

Evaluation criteria, excellent (5)

Excellent 5
In addition to previous requirements you can analytically discuss about the concepts in AI & Machine learning services in the cloud and present and defend (based on knowledge and evidence) your opinions. You can question the presented information and give valid options.

Prerequisites

-Web Development Tools
-SQL Basics
-Basics of programming
-Use of command prompt and/or terminal