Machine learning in web applicationsLaajuus (4 cr)
Code: HTKA0220
Credits
4 op
Teaching language
- Finnish
Responsible person
- Tommi Tuikka
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.
Timing
21.10.2024 - 18.12.2024
Number of ECTS credits allocated
4 op
Mode of delivery
Face-to-face
Unit
School of Business
Campus
Main Campus
Teaching languages
- Finnish
Seats
20 - 40
Degree programmes
- Bachelor's Degree Programme in Business Information Technology
Teachers
- Tommi Tuikka
Groups
-
ZJA24SIAvoin amk, tiko
-
HTK23S1Tietojenkäsittely (AMK)
Objectives
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.
Time and location
Autumn 2024
Teaching methods
Video lectures and contact hours with guidance
The course can also be completed entirely online. The course includes pre-recorded video lectures. In addition, the course includes a 0.5-1 hour personal assessment interview with the teacher via Zoom.
Student workload
108 hours
Further information for students
The exercises will be assessed in a personal assessment interview. Understanding the solutions to the exercises and answering the teacher's questions will be the main focus of the assessment. Simply completing the exercises, e.g. with the help of AI, does not guarantee a grade.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
(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.
Evaluation criteria, good (3-4)
(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.
Evaluation 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.
Prerequisites
Data structures and algorithms
Basics of backend and frontend web development
Enrollment
01.08.2023 - 24.08.2023
Timing
09.10.2023 - 19.12.2023
Number of ECTS credits allocated
4 op
Mode of delivery
Face-to-face
Unit
School of Business
Campus
Main Campus
Teaching languages
- Finnish
Seats
20 - 45
Degree programmes
- Bachelor's Degree Programme in Business Information Technology
Teachers
- Tommi Tuikka
Groups
-
HTK22S1Tietojenkäsittely (AMK)
-
ZJA23SIAvoin amk, tiko
Objectives
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.
Further information for students
Avoin 2
EduFutura 3
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
(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.
Evaluation criteria, good (3-4)
(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.
Evaluation 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.
Prerequisites
Data structures and algorithms
Basics of backend and frontend web development
Enrollment
01.08.2022 - 25.08.2022
Timing
03.10.2022 - 21.12.2022
Number of ECTS credits allocated
4 op
Virtual portion
2 op
Mode of delivery
50 % Face-to-face, 50 % Online learning
Unit
School of Business
Teaching languages
- Finnish
Seats
0 - 45
Degree programmes
- Bachelor's Degree Programme in Business Information Technology
Teachers
- Tommi Tuikka
Groups
-
ZJK22SIKorkeakoulujen välinen yhteistyö, TIKO
-
HTK21S1Tietojenkäsittely (AMK)
-
ZJA22SIAvoin AMK, tiko
Objectives
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.
Time and location
Syksy 2022
Learning materials and recommended literature
Materiaali verkkosivuilla
Teaching methods
Videoluennot ja ohjaustunnit
Student workload
98 tuntia
Further information for students
EduFutura 5
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
(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.
Evaluation criteria, good (3-4)
(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.
Evaluation 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.
Prerequisites
Data structures and algorithms
Basics of backend and frontend web development
Enrollment
02.08.2021 - 05.09.2021
Timing
04.10.2021 - 28.01.2022
Number of ECTS credits allocated
4 op
Virtual portion
4 op
Mode of delivery
Online learning
Unit
School of Business
Teaching languages
- Finnish
Seats
0 - 50
Degree programmes
- Bachelor's Degree Programme in Business Information Technology
Teachers
- Tommi Tuikka
Groups
-
HTK20S1Tietojenkäsittely
Objectives
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.
Time and location
Syksy 2021
Learning materials and recommended literature
Materiaali verkkosivuilla
Teaching methods
Luennot ja ohjaustunnit
Student workload
98 tuntia
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
(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.
Evaluation criteria, good (3-4)
(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.
Evaluation 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.
Prerequisites
Data structures and algorithms
Basics of backend and frontend web development