Claim up to $500 in flight credits!
Get up to $500 in flight credits or grants toward study or internship programs abroad when you apply by November 17, 2024. See our Official Rules for full details.
Statistical Learning
Engineering & Social Sciences Program
Madrid, Spain
Dates: 8/30/21 - 12/23/21
Statistical Learning
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Mathematics
Instruction in: English
Course Code: 17645
Transcript Source: Partner Institution
Course Details: Level 300, 400
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: Basics of Statistics
DESCRIPTION
1. Introduction to the analysis of multivariate data with R.
1.1 Multivariate data.
1.2 Introduction to R for the analysis of multivariate data.
2. Data visualization: Principal Component Analysis.
2.1 Introduction to data visualization.
2.2 Principal Components.
2.3 Practical cases.
3. Data visualization: Multidimensional scaling.
3.1 Multidimensional scaling.
3.2 Practical cases.
4. Classification: linear discriminant analysis.
4.1 Supervised classification.
4.2 Linear discriminant analysis.
4.3 Quadratic discriminant analysis.
4.4 Practical cases.
5. Classification: logistic regression.
5.1 Logistic regression.
5.2 Practical cases.
6. Classification: Naive Bayes classifier.
6.1 Naive Bayes.
6.2 Practical cases.
7. Data segmentation: Cluster analysis. Hierarchical cluster and k-means algorithm.
7.1 Non-supervised classification.
7.2 Hierarchical methods.
7.3 Partitioning methods: K-means algorithm.
7.4 Partitioning methods: Alternatives to k-means.
8. Introduction to non-linear prediction.
8.1 Linear prediction.
8.2 Non-linear prediction.
1.1 Multivariate data.
1.2 Introduction to R for the analysis of multivariate data.
2. Data visualization: Principal Component Analysis.
2.1 Introduction to data visualization.
2.2 Principal Components.
2.3 Practical cases.
3. Data visualization: Multidimensional scaling.
3.1 Multidimensional scaling.
3.2 Practical cases.
4. Classification: linear discriminant analysis.
4.1 Supervised classification.
4.2 Linear discriminant analysis.
4.3 Quadratic discriminant analysis.
4.4 Practical cases.
5. Classification: logistic regression.
5.1 Logistic regression.
5.2 Practical cases.
6. Classification: Naive Bayes classifier.
6.1 Naive Bayes.
6.2 Practical cases.
7. Data segmentation: Cluster analysis. Hierarchical cluster and k-means algorithm.
7.1 Non-supervised classification.
7.2 Hierarchical methods.
7.3 Partitioning methods: K-means algorithm.
7.4 Partitioning methods: Alternatives to k-means.
8. Introduction to non-linear prediction.
8.1 Linear prediction.
8.2 Non-linear prediction.