Brief Course Description
1. Course Title:
Statistical Learning
2. Language of Instruction:
Tiếng Việt
3. Course Code:
MAT708006
4. Credits:
3
5. Course Objectives:
6. Brief Description of Course Content:
Statistical learning is a recently developed field of Statistics, closely associated with the development of computer science and especially machine learning. This course introduces some of the important tools/models for the purpose of analyzing complex data sets. The course content includes both theoretical basis and application of these tools/models. They will not be seen as black-boxes, instead learners will understand more deeply instead of just using them. The main reason is this: there is no tool or model that will work well for all practical situations, without understanding how the tool works it is difficult to choose the most appropriate tool for a given situation. specific situation. Tools/models to be covered include:
- Supervised learning algorithms: linear regression, logistic regression; linear discriminant analysis; cross-validation; bootstrap; linear model selection and regularization; nonlinear models as regression splines; decision trees, support vector machine, and neural networks.
- Unsupervised learning algorithms: dimensionality reduction, clustering.
Students will be guided to practice on the Python/R programming language.