Curricula

Objective

Purpose of the course

Are you interested in learning how to develop intelligent web applications that make decisions based on data? The use of artificial intelligence and machine learning will become an increasingly important part of the work of web application developers in the future. This course introduces you to machine learning algorithms and neural networks on the client and server side of web applications, as well as to open source machine learning models that are ready to be used. After completing the course, you will be able to develop data-analysing web applications using a machine learning library and ready-made open source machine learning models.

Course competences

- Application development: Knows the technologies used in application development and recognizes the significance of different technologies and their relationships.
- Information systems competence: Is familiar with typical information systems and services and understands the importance of security in utilizing services.
- ICT Specialization: Is able to apply their knowledge and skills in a specific area of ICT, as well as analyze, evaluate, and develop operations in this area.
- Learning to learn: Is able to acquire, critically assess and appropriately apply the national and international knowledge base and practices of their field.

Learning outcomes

The student is able to implement web applications using different data sources and machine learning algorithms to analyse data for both client and server side. The student can exploit neural networks with the help of a machine learning library and can use cloud platform services in the implementation of machine learning applications. Students will be familiar with the most common types and uses of machine learning algorithms and will be able to exploit them in appropriate situations.

Content

The course will provide you with the basic skills to use AI and machine learning services in web applications. Content includes data pre-processing and analysis, classical machine learning, neural network-based machine learning in browser and server applications, and the use of open source machine learning models in web applications.

Qualifications

Backend and frontend web application development basics.

Assessment criteria, satisfactory (1)

(Adequate 1) You can implement simple machine learning applications using models presented in lessons or tutorials on the web. You have attempted all the exercises and reached the final result specified in the instructions in at least 50% of the exercises.

(Satisfactory 2) You can implement simple machine learning applications using models presented in lessons or tutorials on the web. You can independently use ready-made machine learning models. You have attempted all the exercises and reached the final result specified in the instructions in at least 70% of the exercises.

Assessment criteria, good (3)

(Good 3) You can implement basic machine learning applications and can apply the technologies learned to the development of slightly more demanding applications. You can independently use ready-made machine learning models. You have attempted all the exercises and reached the final result specified in the instructions in at least 80% of the exercises.

(Commondable 4) You can implement basic machine learning applications and can apply the technologies learned to the development of more demanding applications. You can make extensive use of existing machine learning models. You are able to extend your knowledge beyond the topics covered in the course on your own initiative. You have attempted all the exercises and reached the final result specified in the instructions in at least 90% of the exercises.

Assessment criteria, excellent (5)

(Excellent 5) In addition to the previous requirements, you can critically evaluate machine learning algorithms and off-the-shelf machine learning models, and understand the criteria for selecting technologies for different applications. You have completed all the exercises and have reached the final result as specified in the instructions in all of them.