In this course, you will learn how machine learning and statistics are related to each other. You will be (re-)introduced to the various aspects of practical statistics e.g. how to detect outliers, impute missing values, data scaling, sampling, hypothesis testing, parameter estimation, etc. You will also need to understand probability and perform correlation/regression-type analyses. Finally, you need to understand the practice-theory gap, where statistical theory fails in real-world applications, and how to minimize such occurrences.

This is a Technology-Enhanced Learning (TEL) course (100%).

Aims and objectives

1. You will learn how machine learning and statistics are related
2. You will gain grounding in both descriptive and inferential statistics
3. You will understand how theoretical statistics may mislead in real world applications

Syllabus

1. Statistics and machine learning
2. (Re-)Introduction to statistics
3. Probability
4. The normal distribution and descriptive statistics
5. Correlation
6. Inferential Statistics 1: Hypothesis-testing
7. Inferential Statistics 2: Parameter estimation
8. Non-parametric statistics
9. P-value instability and reproducible research
10. The Anna Karenina Principle

Assessment

Continuous Assessment (multiple response questions and/or structured short answer questions; 1 set per lecture Class participation	Individual Individual	80% 20%
		100%