BD-2-005 (0) - Machine Learning for Predictive Problems

• Big data analytics 2016 - Big data analytics
• Big data analytics 2017 - Big data analytics
• Big data analytics 2018 - Big data analytics
• Big data analytics 2019 - Big data analytics
• Big data analytics 2020 - Big data analytics
• Big data analytics 2021 - Big data analytics - specialised professional studies
• Big data analytics 2022 - Big data analytics - specialised professional studies
• Big data analytics 2023 - Big data analytics - specialised professional studies

• 1 (2023-08-01 till 2023-10-22)
• 2 (2023-10-23 till 2023-12-31)

Engelska

Obligatorisk

Högre yrkeshögskoleexamen

1

5 sp

The aim of the course is that the student will be
able to apply machine learning models and create a
proper pipeline for prediction from static data.

Knowledge
At the end of the course the student is expected
to be able to understand and predict with data
using machine learning models. The student will be
able to select the proper model and metric based
in the nature of the problem and optimize its
parameters using pipelines with grid-search.

The students learn to understand the nature of
data flow by working with streaming data. The
students understand how regression is performed
and how to deal with time series. The students
gain an understanding of how ensemble models
can improve forecast results for fully
automatized systems.

None

Introduction to Machine Learning with Python A
Guide for Data Scientists by Sarah Guido,
Andreas Müller.

An Introduction to Statistical Learning with
Applications in R by Gareth James, Daniela
Witten, Trevor Hastie and Robert Tibshirani,
Springer.

The Elements of Statistical learning by by
Trevor Hastie, Robert Tibshirani, Jerome
Friedman, Springer.

Deep Learning with Python by François Chollet,
Manning publications.

• Föreläsningar - 30 timmar
• Individuell handledning och grupphandledning - 7 timmar
• Projekt- och produktionsarbete/konstnärlig verksamhet - 30 timmar
• Självstudier - 60 timmar

• Kursens totala antal arbetstimmar: 127 timmar
• Varav självstyrda studieformer: 127 timmar
• Varav schemalagda studier: 0 timmar

Flerformsundervisning (delvis nätundervisning handledd eller självstudier)

To pass the course the student should pass the
following examinations: Jupyter notebooks solving
a problem during the lectures and homeworks with
associated content.

• Espinosa Leal Leonardo
• Scherbakov-Parland Andrej
• Pham Truong An

Espinosa Leal Leonardo

https://arcada.itslearning.com/ Extern länk

Ingen begränsning (41 studenter anmälda)

12 månader efter kursens slutdatum

2023-10-05 till 2023-10-12