Applied Machine Learning
7.5 ECTS creditsThe course provides an introduction to machine learning with a focus on applied deep machine learning.
The first part of the course covers the basics of machine learning, including relevant theory, terminology, and principles, and introduces both classical and deep machine learning. Instruction is mainly in the form of lectures and self-study of educational materials based on open-access sources. Some parts involve flipped classroom activities.
The second part of the course focuses on applied deep machine learning through a number of practical exercises and combines lectures on necessary theory with laboratory sessions. The aim is to enable students to explore problem solution independently through using popular libraries and tools with open source code for deep machine learning.
The course concludes with a laboratory task focused on deep machine learning which is presented orally in groups.
The first part of the course covers the basics of machine learning, including relevant theory, terminology, and principles, and introduces both classical and deep machine learning. Instruction is mainly in the form of lectures and self-study of educational materials based on open-access sources. Some parts involve flipped classroom activities.
The second part of the course focuses on applied deep machine learning through a number of practical exercises and combines lectures on necessary theory with laboratory sessions. The aim is to enable students to explore problem solution independently through using popular libraries and tools with open source code for deep machine learning.
The course concludes with a laboratory task focused on deep machine learning which is presented orally in groups.
Progressive specialisation:
G2F (has at least 60 credits in first‐cycle course/s as entry requirements)
Education level:
Undergraduate level
Admission requirements:
60 ECTS credits completed including Data Structures and Algorithms, 7.5 ECTS credits, and Discrete Mathematics, 7.5 ECTS credits, or equivalent
Selection:
Selection is usually based on your grade point average from upper secondary school or the number of credit points from previous university studies, or both.
This course is included in the following programme
- Study Programme in IT-Design: Software Design (studied during year 3)
- Master of Science in Computer Engineering (studied during year 3)
- Study Programme in Engineering - Computer Science (studied during year 3)
- Bachelor Programme in Computer Science (studied during year 3)