EEE3030 : Signal Processing and Machine Learning
- Offered for Year: 2024/25
- Module Leader(s): Professor Jeffrey Neasham
- Lecturer: Dr Kabita Adhikari
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 10 |
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
To develop in-depth knowledge of discrete-time signal processing algorithms, and approaches to measure deterministic and random signals in the frequency domain.
To measure the computational cost of different algorithms used in time/frequency transformation.
To gain proficiency in methods to distinguish the desired signals from noise using appropriate digital filters.
Using problem-based learning to gain skills in the design and implementation of digital signal processing systems for real world applications.
This module also aims to provide underlying mathematical, statistical, and theoretical concepts of Machine Learning along with essential programming skills and expertise to design, build, and implement appropriate Machine Learning techniques for various engineering applications. The module introduces classical regression, classification, and clustering models. The module includes relevant programming exercises to complement the theoretical concepts, which will allow students to gain valuable conceptual and programming skills to build, optimise and implement these models into a range of practical engineering challenges.
Outline Of Syllabus
In semester 1 the module introduces the mathematical foundations and concepts of discrete time signals, digital signal analysis and digital filtering. Sampling and quantisation effects are discussed before moving on to time and frequency domain analysis techniques for deterministic, periodic and random waveforms. Fixed digital filter structures are introduced along with design methods/tools used to achieve the required magnitude and phase response. Adaptive filter structures are introduced as a means of achieving optimum filtering and noise cancellation for unknown and time varying systems. The computational efficiency of these techniques is explored throughout along with methods to minimise computation, including fast transforms and multi-rate systems.
In semester 2, the module introduces mathematical foundations and fundamental concepts of machine learning, including supervised, unsupervised, and clustering methods. The module focuses on mathematically formulating, and practically designing, testing and validating different types of machine learning algorithms to solve various real-world application. It also covers data processing and compression techniques to interpret key information extracted by different machine learning models. The module also exposes the ethical, privacy and security related issues of data driven machine learning models.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 17:00 | 17:00 | Completion and review of formative assessment (online Quizzes) (Sem II) |
| Structured Guided Learning | Lecture materials | 27 | 0:20 | 9:00 | Non-synchronous: 20 mins pre-recorded videos consisting of theory on DSP topics & MATLAB demonstrations |
| Scheduled Learning And Teaching Activities | Lecture | 11 | 2:00 | 22:00 | Discussions and Q&A sessions + Computing lab sessions for programming exercises (Sem II) |
| Structured Guided Learning | Lecture materials | 30 | 0:20 | 10:00 | Pre-recorded video lectures, students’ Asynchronous online study (Sem II) |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:00 | 2:00 | Written Exam (Sem II) |
| Guided Independent Study | Assessment preparation and completion | 10 | 1:30 | 15:00 | Revision for final exam (Sem II) |
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Coursework consisting of a DSP system design & verification in MATLAB. |
| Guided Independent Study | Directed research and reading | 27 | 0:20 | 9:00 | Student study time of non-synchronous pre-recorded material. |
| Scheduled Learning And Teaching Activities | Practical | 11 | 2:00 | 22:00 | Present in person seminar and practical lab session – each session will review lecture materials, DSP exercises and students’ questions, then students will work on MATLAB based DSP exercise under supervision of lecturer. |
| Guided Independent Study | Independent study | 34 | 1:00 | 34:00 | Lectures follow up: Reviewing lecture materials, building understanding, and creating comments on provided lecture documents (Sem II) |
| Guided Independent Study | Independent study | 1 | 40:00 | 40:00 | Reviewing lecture notes, online materials, general background reading and work on MATLAB exercises. |
| Total | 200:00 |
Teaching Rationale And Relationship
Guided online study provides the fundamental concepts of the course while the seminars and Q&A sessions reinforce understanding and application context. Lab sessions and MATLAB based exercises will develop skills in design, implementation and testing of signal processing and machine learning algorithms.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 120 | 2 | A | 75 | Final exam covering machine learning, optimal and adaptive filters. |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Written exercise | 1 | M | 25 | MATLAB based signal analysis/filtering task assessed by written report. |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Prob solv exercises | 1 | M | Weekly exercises to gain familiarity with signal processing methods and MATLAB programming. |
| Prob solv exercises | 2 | M | Students will complete the set programming exercises to design and verify Machine learning models |
| Digital Examination | 2 | M | Students will complete online quizzes to check their understanding of the taught materials |
Assessment Rationale And Relationship
The summative assignment (25%) assess core understanding of course material, analysis/design skills applied to realistic DSP problems and their ability to simulate and verify algorithm performance in MATLAB.
The final NUMBAS-based exam will evaluate students' knowledge and understanding of statistical signal analysis, optimal digital filtering techniques, and the fundamental principles and applications of machine Learning techniques. Additionally, the exam will assess students' abilities to formulate, design, and select various machine learning models, as well as their capacity to interpret key information derived from these models. Furthermore, it will test students' capability in evaluating ethical and societal issues associated with data-driven machine learning models.
The formative MATLAB exercises will provide weekly feedback on their understanding of the DSP topics and their readiness to take on the assignment. The machine learning programming exercises will reinforce understanding of the machine learning methods and their practical implementation/testing. The online quizzes will provide feedback to students on their grasp of these topics and readiness for the exam.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- EEE3030's Timetable