Machine learning in web applicationsLaajuus (4 cr)

Objective

The purpose of the course
Are you interested in learning how to develop smart data-driven web applications? Utilizing artificial intelligence and machine learning will be an increasingly important part of a web application developer’s work in the future. The course introduces machine learning algorithms and neural networks on the client and server side of web applications and on a cloud service platform. After completing the course, you will be able to develop data analysis web applications with the help of a machine learning library and services provided by the cloud platform.

Course competencies
Application development expertise

Course competence
The student is able to implement web applications that analyze data using various data sources and machine learning algorithms for both client and server side. The student is able to utilize neural networks with the help of a machine learning library and is able to use the services of a cloud platform in the implementation of machine learning applications. The student knows the most common types and applications of machine learning algorithms and is able to utilize them in suitable use situations.

Content

The course teaches the development of web applications based on machine learning and artificial intelligence. The content includes e.g. data pre-processing and analysis, classical machine learning, machine learning based on neural networks in a browser application and a server application, and utilization of cloud platform machine learning and artificial intelligence services in web applications. The course provides basic skills for utilizing machine learning and artificial intelligence services in web application development.

Qualifications

Data structures and algorithms
Basics of backend and frontend web development

Assessment criteria, satisfactory (1)

(Adequate 1) The student is able to implement basic machine learning applications using the models presented in the lessons or tutorials on the web. He has tried to do all the exercises and reached the result determined by the instructions in at least 50% of the tasks.

(Satisfactory 2) The student is able to implement basic machine learning applications using models presented in lessons or tutorials on the web. He has tried to do all the exercises and reached the result determined by the instructions in at least 70% of the tasks.

Assessment criteria, good (3)

(Good 3) The student is able to implement basic machine learning applications and is able to apply the learned technologies also in the development of more demanding applications. He is able to expand his knowledge on his own initiative beyond the things presented in the course. He has tried to do all the exercises and reached the result specified in the instructions in at least 80% of the tasks.

(Commendable 4) The student is able to implement basic machine learning applications and is able to apply the learned technologies also in the development of more demanding applications. He is able to expand his knowledge on his own initiative beyond the things presented in the course. He has tried to complete all the exercises and reached the result specified in the instructions in at least 90% of the tasks.

Assessment criteria, excellent (5)

(Excellent 5) In addition to the above requirements, the student is able to critically evaluate machine learning algorithms and cloud platform tools and understand the selection criteria of technologies for different applications. He has completed all the exercises and reached the end result specified in the instructions.