IBE400 Machine Learning (Autumn 2024)

About the course

This is applied machine learning course that is designed to provide students with an overview of the fundamental concepts and techniques of machine learning. It covers a range of topics, including supervised and unsupervised learning, classification, regression, clustering, neural networks, and reinforcement learning. The course emphasizes the use of algorithms to analyze data sets, identify patterns and relationships, and make inference based on that data. In addition to covering the basic principles of machine learning, the course also explores data preprocessing techniques, feature selection methods, and model evaluation strategies. Through a combination of lectures, practical exercises, and case studies, students will gain practical experience in applying machine learning techniques to real-world problems.

The course is connected to the following study programs

• Individual Study Courses/Part- time studies
• Bachelor in IT and Digitalization
• Exchange programme - Bachelor's level

Recommended requirements

You should have basic skills with programming languages such as Python, or R or Matlab, statistics and mathematics (including calculus, probability, and statistical inference).

It is also beneficial to have a background in data analysis and visualization.

The student's learning outcomes after completing the course

Students will after passed examination, have

Knowledge

• Solid foundation in the basic machine learning principles and practical skills.

• Analyze data sets using a range of machine learning algorithms

• Identify patterns and relationships in data

• Understanding of the role of data preprocessing, feature selection, and model evaluation in machine learning

Skills

• Demonstrate the ability to describe the machine learning techniques of Linear classification, Logistic regression, Decision Trees, Multi-class Classification, Naïve Bayes, Neural Networks, Clustering, Principal Component Analysis & Autoencoders, Support Vector Machines, Ensemble methods, Reinforcement Learning

• Use machine learning algorithms to develop programs that effectively solve real-world problems.

Competence

• Be able to apply ML techniques to real-world data sets.

Forms of teaching and learning

Four hours lecture per week with lectures, lab sessions and mandatory assignments & project work.

Students are expected to attend lectures and sessions in the classroom. The lectures or sessions will not be streamed or recorded.

 

 

 

 

Coursework requirements - conditions for taking the exam

• Mandatory work requirement: Group Project (1-3 students)

• Number of work requirements: 1

• Required work requirements: 1

• Grading: Approved/Not approved

• Comment: Project report
  Project work will provide the opportunity to learn by doing, where students will implement one or more machine learning algorithms and apply them to data, either by comparing various existing algorithms or proposing a new approach and contrasting it with at least one other method and present the outcomes in a research paper format. The project can be executed individually or collaboratively, with a higher standard of expectations for collaborative projects.

 

• Mandatory coursework: Assignments.Group of 1-3 students

• Courseworks given: 2

• Courseworks required: 2

• Grading: Approved/Not approved

Examination

• Form of assessment: School assessment

• Proportion: 100%

• Duration: 3 hours

• Grouping: Individual

• Grading scale: Letter (A - F)

• Support material:None

 

Syllabus

The current reading list for 2024 Autumn can be found in Leganto