Course info

 Statistical learning provides a probabilistic and statistical understanding of topics in machine learning.

The overarching goal of the course is to develop methods for estimating (or 'learning') an unknown function from data or making predictions for unseen function outputs. The course aims to empower the student to make a justified decision in adopting machine learning approaches for practical problems and even design their own machine learning methodology using sound mathematical principles. This is achieved by studying key ideas, models, algorithms and theories related to the subject of statistical learning.

In the first half of the course a collection of essential models, algorithms and techniques is introduced. In the second half of the course we adopt a Bayesian perspective on machine learning and study associated methods for analysing models and developing computational methodology.

Study Goals

By the end of the course, you are able to:

1. Explain the challenges of statistical learning (e.g., curse of dimensionality, overfitting, bias-variance trade-off).

2. Implement machine learning algorithms and computational methods on artificial and real-world data sets (e.g., linear regression, logistic regression, sparse regression, neural networks, decision trees).

3. Compare the theoretical properties and practical aspects of machine learning approaches.

4. Validate machine learning approaches on arbitrary data sets.

5. Design tailor-made computational approaches for new learning problems by adapting, combining and/or extending methods and/or by employing a Bayesian approach.