Who is this course for?
This course provides a broad introduction to machine learning (ML). It is intended for people with a background in computer science, who have not studied ML and AI before and want to gain skills in this area and understand how ML techniques work under the hood.
What will you learn from this course?
Students will learn about standard supervised machine learning techniques (for classification and regression), some unsupervised learning techniques (for clustering and anomaly detection), as well as best practices to achieve a good generalization and avoid underfitting/overfitting. Students will also gain practice implementing these techniques in Python and getting them to work on real data.
What is the format for this course?
Instruction type: The course is delivered fully online and in English. It consists of lecture sessions delivered through Zoom, pre-recorded videos, quizzes, and Lab assignments.
Frequency: The course consists of weekly lectures and assignments, distributed over a total of 9 weeks.
Examination: The exams consist of a written examination and Lab assignments. The overall grades of Pass/Fail is awarded for this course.
Email: mohamed-rafik.bouguelia [AT] hh [DOT] se