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 ListCan we help you?
Ask us about studying at Dalarna
support@du.se
+46 23-77 88 88
Course room in Learn
In the learning platform Learn you can find more information about the course.
Visit the course room
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
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 ListCan we help you?
Ask us about studying at Dalarna
support@du.se
+46 23-77 88 88