F20DX - Data Mining and Machine Learning

Course leader(s):

Diana Bental (Edinburgh, January)

Aims

This is a work-based Learning (WBL) course to be delivered by work-based blended on-line learning materials, with the following aims:

- To introduce students to the fundamental concepts and techniques used in data mining and machine learning.

- To develop a critical awareness of the appropriateness of different data mining and machine learning techniques.

- To provide familiarity with typical and relevant applications of data mining and machine learning techniques.

Syllabus

1. Basic concepts and workflow (1.1 Ddatasets, models, data quality, dealing with missing data, classification, statistics, feature selection, supervised and unsupervised learning, evaluating models, quality and reliability measures.)

2. Learning methods (2.1 Foundations of regression analysis, cluster analysis, naïve Bayes, k-nearest-neighbour, decision trees, neural networks, association rule mining. An introduction to advanced methods such as ensemble methods and deep learning architectures. Methods will be related to applications which are relevant to the students’ own industrial context.)

3. Applications (3.1 Relevant and typical applications, for example web analytics and recommender systems, market basket analysis, diagnostics, image processing, text analysis.)

Learning outcomes

By the end of the course, students should be able to do the following:

construct key building blocks for data mining and machine learning including data preparation, feature extraction, model training and evaluation.
differentiate between some popular machine learning approaches related to supervised and unsupervised learning.
practice the mathematics underpinning methodologies of data mining and machine learning.
assess the benefits and limitations of using a variety of machine learning techniques for solving practical applications in commercial or industrial contexts,
choose appropriate solutions for practical problems based on complex issues like data quality, model complexity and overfitting.
select a variety of software libraries to process data and evaluate machine learning pipelines.
apply a professional and ethical approach to the use and management of data.

Further details

Curriculum explorer: Click here

SCQF Level: 10

Credits: 15