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Programme Specification (2021-22)
Programme Information
Programme Title Programme Code HECoS Code
Applied Machine Learning I For Registry Use Only
Award Length of Study Mode of Study Entry Point(s)
Total Credits
ECTS CATS
MSc 1 Calendar Year ( months) Full-Time Annually in October
Ownership
Awarding Institution Imperial College London Faculty Faculty of Engineering
Teaching Institution Imperial College London Department Electrical and Electronic Engineering
Associateship N/A Main Location(s) of Study
South Kensington Campus
External Reference
Relevant QAA Benchmark Statement(s) and/or other external reference points Master’s Awards in Engineering
FHEQ Level Level 7 - Master’s
EHEA Level 2nd Cycle
External Accreditor(s) (if applicable)
External Accreditor 1: N/A
Accreditation received: N/A Accreditation renewal: N/A
Collaborative Provision
Collaborative partner Collaboration type Agreement effective date
Agreement expiry date
N/A N/A N/A N/A
Specification Details
Programme Lead Prof Krystian Mikolajczyk, Dr Ad Spiers
Student cohorts covered by specification 2021/22 entry
Date of introduction of programme 01/10/
Date of programme specification/revision October 2021
Programme Overview
This MSc programme will provide essential training and skills to design, implement and evaluate machine learning systems in several application domains (robotics, communication, speech and vision). It will be delivered within the department of Electrical and Electronic Engineering.
The programme consists of compulsory taught modules that provide general background theory, practical knowledge and skills (classical Machine Learning and Deep Learning), compulsory modules focusing on machine learning applied in engineering domains and delivered by internationally leading experts in their respective research fields (robotics, communication, speech and vision) and optional modules that allow students to broaden their experience with other applications of machine learning (AI, neuroscience and signal processing). The modules have a coursework component with the majority of the application modules assessed by coursework only.
The programme is intended for graduates in broad electrical and electronic engineering domains with substantial mathematics and engineering content that require machine learning knowledge and skills.
Most industries working with large amounts of sensors that produce data have recognized the value of machine learning technology. By intelligent processing of the data, organizations are able to offer new products with enhanced capabilities, optimize their processes and gain an advantage over competitors. These include manufacturing, communications, creative industries, health care, energy management, transportation etc. The data analysis and modelling aspects of machine learning are important tools to optimise and automise processes that most industries and services rely on.
During this course, students will focus on applying machine learning to electrical engineering. Applications include robotics, computer vision bio-inspired learning, communication and signal processing. This course is intended for graduates interested in developing real-world systems. These will involve signals, sensors and hardware, such as robots or communication devices.
Learning Outcomes
Upon successful completion of the programme you will be able to:
- Apply fundamental concepts and theoretical principles of machine learning for building signal and data representations and modelling target functions;
- Develop insight into the problems involved in applying a variety of machine learning techniques (such as neural networks, etc.) to deal with practical scenarios;
- Critically analyse suitable EEE tasks to which ML techniques can be applied;
- Formulate practical EEE problems as machine learning tasks;
- Calculate theoretical values of a learning model given input data and parameters;
- Analyse and compare the strengths and weaknesses of popular approaches;
- Design and implement various algorithms in a range of EEE applications through specific programming environments;
- Predict potential outcomes of applying various types of techniques to a given problem;
- Create data from various sensors for training modern machine learning models;
- Evaluate the effectiveness of a particular implementation through appropriate design and execution of experiments;
- Analyse and document evaluation results, draw appropriate conclusions and recommend actions to improve the performance.
The Imperial Graduate Attributes are a set of core competencies which we expect students to achieve through completion of any Imperial College degree programme. The Graduate Attributes are available at: www.imperial.ac.uk/students/academic-support/graduate-attributes
Overall Workload Your overall workload consists of face-to-face sessions and independent learning. While the actual contact hours may vary according to the optional modules chosen to study, the following gives an indication of how much time will need to be allocated to different activities at each level of the programme. At Imperial, each ECTS credit taken equates to an expected total study time of 25 hours. Therefore, the expected total study time is 2,250 hours per year.
Assessment Strategy
Assessment Methods
This programme will employ both summative and formative assessments to support and assess your learning. The goal of the summative assessment is to award a grade against a set of criteria, which includes forms of written exams, coursework, laboratory work, written reports and oral presentation. The formative assessment is designed to support you to better perform in your summative assessment to meet the learning outcomes of the programme.
Assessment of the knowledge base is through a combination of unseen written examinations and assessed coursework. Assessment of intellectual, practical and transferable skills is through coursework and supervised project work.
Autumn modules are focused on theory and have significant part of assessment by written examinations. Spring modules are mainly assessed by coursework. The Summer term is full time project work.
Academic Feedback Policy
Written feedback will be available through Blackboard within a week of the submission of coursework assignment with a maximum time limit of two weeks. This will be in the form of, for example:
- Marked-up coursework, laboratory exercises or tests
- Personal discussion
- Discussions in small-group tutorials
- Verbal presentation, e.g. during or after lectures
- Written class-wide summaries
In lieu of feedback on examinations, selected examination questions are routinely set as unassessed problems in later years, with model answers provided.
The College’s Policy on Academic Feedback and guidance on issuing provisional marks to students is available at: www.imperial.ac.uk/about/governance/academic-governance/academic-policy/exams-and-assessment/
Re-sit Policy
The College’s Policy on Re-sits is available at: www.imperial.ac.uk/student-records-and-data/for-current- students/undergraduate-and-taught-postgraduate/exams-assessments-and-regulations/
Mitigating Circumstances Policy
The College’s Policy on Mitigating Circumstances is available at: www.imperial.ac.uk/student-records-and- data/for-current-students/undergraduate-and-taught-postgraduate/exams-assessments-and-regulations/
Additional Programme Costs
This section should outline any additional costs relevant to this programme which are not included in students’ tuition fees.
Description Mandatory/Optional Approximate cost
N/A N/A N/A
Important notice : The Programme Specifications are the result of a large curriculum and pedagogy reform implemented by the Department and supported by the Learning and Teaching Strategy of Imperial College London. The modules, structure and assessments presented in this Programme Specification are correct at time of publication but might change as a result of student and staff feedback and the introduction of new or innovative approaches to teaching and learning. You will be consulted and notified in a timely manner of any changes to this document.
Progression and Classification
Student progression and academic development will be monitored through formative and summative assessment. The following criteria for MSc are currently in place in EEE department.
MSc
- At least 40% for each of the 8 modules counted for the computation of the examinations average
- At least 50% for the laboratory average
- At least 50% for both the project and examinations average
MSc with Merit
- At least 40% for each of the 8 modules counted for the computation of the examinations average
- At least 50% for the laboratory average
- At least 60% for both the project and examinations average
MSc with Distinction
- At least 40% for each of the 8 modules counted for the computation of the examinations average
- At least 50% for the laboratory average
- At least 70% for both the project and examinations average
Programme Specific Regulations
N/A
Supporting Information
The Programme Handbook is available at: TBC
The Module Handbook is available at: http://intranet.ee.ic.ac.uk/electricalengineering/eecourses_t4/index.asp
The College’s entry requirements for postgraduate programmes can be found at: www.imperial.ac.uk/study/pg/apply/requirements
The College’s Quality & Enhancement Framework is available at: www.imperial.ac.uk/registry/proceduresandregulations/qualityassurance
The College’s Academic and Examination Regulations can be found at: www.imperial.ac.uk/about/governance/academic-governance/regulations
Imperial College is an independent corporation whose legal status derives from a Royal Charter granted under Letters Patent in 1907. In 2007 a Supplemental Charter and Statutes was granted by HM Queen Elizabeth II. This Supplemental Charter, which came into force on the date of the College's Centenary, 8th July 2007, established the College as a University with the name and style of "The Imperial College of Science, Technology and Medicine". www.imperial.ac.uk/admin-services/secretariat/college-governance/charters/
Imperial College London is regulated by the Office for Students (OfS) www.officeforstudents.org.uk/advice-and-guidance/the-register/
This document provides a definitive record of the main features of the programme and the learning outcomes that a typical student may reasonably be expected to achieve and demonstrate if s/he takes full advantage of the learning opportunities provided. This programme specification is primarily intended as a reference point for prospective and current students, academic and support staff involved in delivering the programme and enabling student development and achievement, for its assessment by internal and external examiners, and in subsequent monitoring and review.
Modifications
Description Approved Date Paper Reference
N/A
