Course AMI22T

Statistical Learning

7.5 Credits
Second Cycle

Starts week 13, 2022

The course focuses mainly on the applied aspects of statistical learning. However, the most important basic properties of, and relations between different statistical learning models and algorithms are also included. The course covers supervised learning algorithms, with special emphasis on classification methods such as logistic regression, classification trees, linear discriminant analysis, quadratic discriminant analysis, K-nearest neighbour, support vector machine, and regression methods such as linear regression, smoothing splines, generalised additive model, and regression trees. The course also covers unsupervised learning methods such as principal component analysis, k-mean clustering, and hierarchical clustering. Model validation through cross validation, and bootstrap methods are covered. Regularisation for model selection, high dimensional data analysis, and improving prediction performance through model averaging, bagging, and boosting techniques are also covered.
Starts and ends:
week 13, 2022 - week 22, 2022
Study Rate:
50%
Location:
Borlänge
Time of Day:
Day
Teaching form:
Normal
Language:
English
Entry Qualifications:
  • Bachelor‘s degree or courses comprising 180 credits
Application Code:
HDA-V3A73
Main field of study:
Literature List

Literature lists are published at the latest one month ahead of the course start date.

To Literature List
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Ask us about studying at Dalarna
support@du.se
+46 23-77 88 88

Course room in Learn

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Course Coordinator

Starts week 13, 2023

The course focuses mainly on the applied aspects of statistical learning. However, the most important basic properties of, and relations between different statistical learning models and algorithms are also included. The course covers supervised learning algorithms, with special emphasis on classification methods such as logistic regression, classification trees, linear discriminant analysis, quadratic discriminant analysis, K-nearest neighbour, support vector machine, and regression methods such as linear regression, smoothing splines, generalised additive model, and regression trees. The course also covers unsupervised learning methods such as principal component analysis, k-mean clustering, and hierarchical clustering. Model validation through cross validation, and bootstrap methods are covered. Regularisation for model selection, high dimensional data analysis, and improving prediction performance through model averaging, bagging, and boosting techniques are also covered.
Starts and ends:
week 13, 2023 - week 22, 2023
Study Rate:
50%
Location:
Borlänge
Time of Day:
Day
Teaching form:
Normal
Language:
English
Other:
P: Course only offered as part of programme.
Entry Qualifications:
  • Bachelor‘s degree or courses comprising 180 credits
Application Code:
HDA-V3D94
Main field of study:
Tuition Fee
First Tuition Fee Installment:
16,875 SEK
Total Tuition Fee:
16,875 SEK
EU/EEA Citizens or exchange students are not required to pay fees.
Information on application and tuition fees: www.universityadmissions.se.
Literature List

Literature lists are published at the latest one month ahead of the course start date.

To Literature List
Can we help you?

Ask us about studying at Dalarna
support@du.se
+46 23-77 88 88

Course Coordinator