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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Machine Learning and Pattern Recognition (INFR11130)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryMachine learning is a field devoted to developing algorithms that adapt their behaviour to data, providing useful representations of the data and/or predictions. This course covers some fundamental theoretical concepts in machine learning, and common patterns for implementing methods in practice. The intended audience are those wanting the background required to begin research and development of machine learning methods.

This is an advanced course. Students should not choose this class without the required background (see "Other Requirements" box); students without this background are strongly advised to take a more practical-based course, such as Introduction to Applied Machine Learning (MSc INFR11182; UG INFR10069).
Course description The precise set of methods and algorithms used to illustrate and explore the main concepts will change slightly from year to year. However, the main topic headings are expected to be fairly stable.

- Classification and Regression:
Linear Regression, logistic regression, Bayes classifiers
- Expanded feature representations:
Basis functions, neural networks, kernel methods
- Generalization, regularization and inference:
Penalised cost functions, Bayesian prediction, learning theory
- Model selection, pruning and combination:
Cross-validation, Bayesian methods, sparsifying regularizers, ensemble methods.
- Representation and metric learning:
Dimensionality reduction, clustering, feature learning

To support these topics we will also cover:
- Optimization and Inference algorithms:
Stochastic gradient descent, simple Monte Carlo ideas, and more specialised methods as required.
- Practical issues:
Formulating problems as machine learning, adapting methods to fit problems.
Numerical and programming issues important for machine learning.
Ethical issues, such as responsible application of methods and privacy concerns.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Introductory Applied Machine Learning (INFR11182)
Other requirements Undergraduates are advised not to take level 10 IAML (INFR10069) and MLPR (INFR11130) in the same year.

This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
This course requires practical mathematical application of algebra, vectors and matrices, calculus, probability, and problem solving. For example, you will need to be able to differentiate linear algebra expressions with respect to vectors, interpret inner-products and quadratic forms geometrically, and compute expectations of linear algebra expressions under simple distributions. Some of the required details can be learned during the course. However, practical mathematical skills take time to accumulate and a strong mathematical background is essential.
Practical exercises usually require using a particular numerical language such as Matlab or Python+NumPy. We will assume and require sufficient past programming experience that a new package can be learned on the fly.
Course Delivery Information
Academic year 2020/21, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 30, Seminar/Tutorial Hours 10, Feedback/Feedforward Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 154 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) The assessment will consist of weekly assessments (80%) and a time-limited class test (20%).

Most weeks will involve writing explanations, mathematical work, and programming. Some questions will have immediate automated feedback, with further feedback on other questions from peers or instructors. Some credit will be associated with discussion, comparison and reflection.
Feedback Some of the lecture time will be devoted to discussing questions, including some exam-like questions, and providing feedback on student answers. Students will also get feedback on their work through the tutorials.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Frame an applied problem as a machine learning task, identifying appropriate methods.
  2. Critically compare and contrast alternative machine learning methods for a given task.
  3. Derive and motivate novel variants of machine learning methods.
  4. Create accessible and useful explanations of the workings and failure modes of machine learning methods.
  5. Check and refine implementations of learning algorithms, while applying them in practice.
Reading List
- Pattern Recognition and Machine Learning, Christopher Bishop.
- Machine Learning: A Probabilistic Perspective. Kevin P Murphy.
- Bayesian Reasoning and Machine Learning. David Barber.
- A First Course in Machine Learning (2nd Ed.), Simon Rogers and Mark Girolami.
- Information Theory, Inference and Learning Algorithms, David MacKay
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Hastie, Tibshirani, and Friedman.
Additional Information
Course URL
Graduate Attributes and Skills The student will be able to reason about how to make predictions from and interpret data, an important transferable skill.

In addition the student will be able to:

Undertake critical evaluations of a wide range of numerical and graphical data.
Apply critical analysis, evaluation and synthesis to forefront issues, or issues that are informed by forefront developments in the subject/discipline/sector.
Identify, conceptualise, and define new and abstract problems and issues.
Develop original and creative responses to problems and issues.
Critically review, consolidate and extend knowledge, skills, practices and thinking in subject/discipline/sector.
Deal with complex issues and make informed judgements in situations in the absence of complete or consistent data/information.
KeywordsMachine Learning,supervised learning,Probabilistic prediction,Data science
Course organiserDr Iain Murray
Tel: (0131 6)51 9078
Course secretaryMiss Clara Fraser
Tel: (0131 6)51 4164
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