F21DL Data Mining and Machine Learning

Dr Katya KomendantskayaDr Diana BentalNeamat El Gayar

Course co-ordinator(s): Dr Katya Komendantskaya (Edinburgh), Dr Diana Bental (Edinburgh), Neamat El Gayar ().

Aims:

In this course, students will develop:

  • An understanding of the fundamental concepts and techniques used in data mining and machine learning.
  • An understanding of the mathematics underpinning data mining and machine learning.
  • A critical awareness of the appropriateness of different data mining and machine learning techniques and the relationships between them.
  • Familiarity with common applications of data mining and machine learning techniques.

Detailed Information

Pre-requisite course(s): F29AI Artificial Intelligence and Intelligent Agents or basic knowledge of AI concepts and issues.

Location: Dubai, Edinburgh.

Semester: 1.

Syllabus:

Basic Concepts: datasets, dealing with missing data, classification, supervised vs unsupervised learning.

Generative Models: naïve Bayes, probabilistic graphical models, cluster analysis (such as k-means clustering, EM algorithm).

Discriminative Learning: linear regression, decision tree learning, perceptron, advanced models such as multi-layer perceptron and deep learning architectures.

Learning Outcomes: Subject Mastery

Understanding, Knowledge and Cognitive Skills Scholarship, Enquiry and Research (Research-Informed Learning)

  • Extensive understanding of the data mining process and machine learning algorithms.
  • Detailed understanding of the mathematics underpinning the data mining and machine learning methodologies.
  • Critical awareness of the appropriateness and performance of the different techniques, as well as the relationships between them.
  • Critical awareness of data quality and the appropriate use of data mining and machine learning for decision making
  • Ability to apply this knowledge for practical data mining and machine learning purposes

The students will develop their research abilities, and in particular:

  • Ability to conduct quantitative and qualitative research on real-life, complex data sets
  • Ability to ask own research questions about the hidden properties of data
  • Ability to ask own research questions about suitability of certain machine learning methods and algorithms for the given data
  • Demonstrate originality and creativity in the application of knowledge

Learning Outcomes: Personal Abilities

Industrial, Commercial & Professional Practice Autonomy, Accountability & Working with Others Communication, Numeracy & ICT

The students will be expected to:

  • Show capacity for rational problem identification and definition.
  • Show capacity for critical analysis and solution selection, deal with complex issues and make informed judgements.
  • Use appropriate computer software to process data, and to support and enhance the research tasks.
  • Demonstrate the ability to learn independently and demonstrate leadership/initiative in tackling research problems.
  • Manage time, work to deadlines, and prioritise workloads.
  • Use a wide range of resources to present results in a way that demonstrates a good understanding of the technical and broader issues of data mining and machine learning.
  • Communicate with peers and more senior colleagues

Assessment Methods:

Assessment: Examination: (weighting 50%) Coursework: (weighting – 50%)
Re-assessment:  Examination: (weighting – 100%)

SCQF Level: 11.

Credits: 15.