F20DL 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 (Dubai).

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

Course Description: Link to Official Course Descriptor.

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.
  • Awareness of the appropriateness and performance of the different techniques, as well as the relationships between them.
  • 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

Learning Outcomes: Personal Abilities

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

The students will be expected to:

  • Demonstrate the ability to learn independently.
  • Show capacity for rational problem identification and definition.
  • Show capacity for critical analysis and solution selection.
  • Manage time, work to deadlines, and prioritise workloads.
  • Use appropriate computer software to process data.
  • Present results in a way that demonstrates a good understanding of the technical and broader issues of data mining and machine learning.

Assessment Methods:

Assessment: Examination: (weighting – 50%) Coursework: (weighting – 50%)
Re-assessment: None

SCQF Level: 10.

Credits: 15.