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Maskininlärning

5 hp

Målet med kursen är att studenten lär sig vanliga vägledda maskininlärningstekniker för regression och klassificering, samt bästa praxis och erfarenhet gällande att implementera maskininlärning i Python på realistiska data. Kursen ingår i programmet MAISTR (hh.se/maistr) där du som deltagare kan läsa hela programmet eller enstaka kurser. Kursen är för yrkesverksamma och ges på distans på engelska. Anmälan är öppen så länge det finns möjlighet att bli antagen.

About the course Machine Learning, 5 credits

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.

The aim of the course is for students to learn about standard supervised Machine Learning (ML) techniques for regression and classification as well as best practices in ML, and gain practice implementing ML in Python to work on real data.

The course covers the following topics:

  • Introduction to machine learning, including basics and prerequisites.
  • Basic aspects of supervised machine learning, including basic regression and classification algorithms.
  • Overfitting and generalization, the bias/variance trade-off, and methods for avoiding overfitting, including regularization. Explanation of how these problems are addressed in various methods, including Support Vector Machines (SVMs), and ensemble methods.
  • Introduction to Neural Networks for supervised learning, as well as an overview of deep neural networks and unsupervised feature extraction with autoencoders.
  • Overview of unsupervised data clustering methods and their applications.

VT 2022 (Distans (Internet), Ortsoberoende, 33%)

Nivå:

Avancerad nivå

Anmälningskod:

23802

Behörighetskrav:

Kandidatexamen i datateknik eller Högskoleingenjörsexamen i datateknik. Programmering 7,5 hp och Matematik 7,5 hp inklusive Linjär algebra. Engelska 6.

Urvalsregler:

Högskolepoäng: 100%

Mer information om urvalsregler.

Startvecka:

Vecka: 03

Obligatoriska sammankomster:

0

Undervisningstid:

Blandade tider

Studieavgift:

För sökande med medborgarskap utanför EU/EES och Schweiz: Mer information om studieavgift

Undervisningsspråk:

Undervisningen bedrivs på engelska.

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