Machine Learning
Welcome
- Course Overview
Introduction
I Supervised Learning
Regression
1 Linear Regression
Practical Work 1
- 1.8 Some basics
- 1.9 Regression
  - 1.9.1 The lm function
  - 1.9.2 Predicting House Value: Boston dataset
2 Multiple Linear Regression
PW 2
- Multiple Linear Regression
- Reporting
Classification
3 Logistic Regression
PW 3
- Social Networks Ads
4 Discriminant Analysis
PW 4
Special Session
5 Session 5
II Dimensionality Reduction
6 Principal Components Analysis
PW 6
III Unsupervised Learning
7 Clustering
PW 7
- Reporting
- \(k\)-means clustering
8 Gaussian Mixture Models & EM
PW 8
9 Hierarchical Clustering
PW 9
Appendix
A Introduction to RStudio
B Review on hypothesis testing
C Use of qualitative predictors
D Model Selection
E Final Grades
F References and Credits
G Other References
Main References & Credits

E Final Grades