MSc — 2027 entry Computer Vision, Robotics and Machine Learning

Expected prior learning:  Module EEE3008–Fundamentals of Digital Signal Processing  or equivalent learning about signal processing. Module purpose:  The module discusses basic concepts, signal processing methods and human computer interaction applications of speech processing and recognition including auditory perception and psychoacoustics. You will be taught how to extract salient features from speech signals, how to design a model of spoken language, how to perform recognition and training, and given an insight into current research on spontaneous speech recognition, such as speaker adaptation and solutions for robustness to noise. Demonstrations, interesting illustrations and working examples will be given. Successful students can either proceed to do PhDs or get jobs in the R & D departments of industry, i.e. jobs that are at a higher level than mere software package operators. The presented techniques have many other applications beyond speech, including expert systems and financial modelling. Module EEEM030 contributes to the development of student’s knowledge in audio and speech processing and recognition, which may be useful for their taking of other modules such as: EEEM071 Advanced Topics in Computer Vision and Deep Learning EEEM004 60 Credit Standard Project EEEM005 AI and AI Programming EEEM066 Fundamentals of Machine Learning EEEM067 AR, VR and Metaverse EEEM068 Applied Machine Learning Module EEEM030 contributes to student knowledge in audio and speech processing and thus useful for students taking 60 credit project (EEEM004) related to audio and speech processing and recognition. EEEM030 is related to EEEM005, EEEM066 and EEEM068 due to the fact that machine learning/AI techniques are used for speech and speaker recognition, therefore, EEEM030 contributes to the development of student knowledge in machine learning/AI, which is beneficial for their taking of machine learning and AI related modules such as EEEM005, EEEM066 and EEEM068. One of the applications of audio and speech processing is to apply it to AR, VR and Metaverse for spatial sound production and reproduction which is a key enabling technology for AR, VR, and Metaverse, for virtual sound reproduction. Therefore, knowledge gained from EEEM030 would be useful for the taking of the module EEEM067. Module EEE030 also benefits from knowledge gained from other modules such as: EEE3008 Digital Signal Processing EEE1033 Computer and Digital Logic EEE1035 Programming  in C EEE3042 Audio and Video Processing EEE3032 Computer Vision and Pattern Recognition The modules EEE1033 and EEE1035 provide students with some useful skills in programming, which will be beneficial for them to complete the computer programming based coursework components, by turning the signal processing theories and methods into working program codes. The module EEE3008 provides students with knowledge and skills on fundamental digital signal processing skills which are essential in understanding the application of these skills to audio and speech data. EEE3042 covers both audio and video processing and coding, and the audio related materials are highly relevant and thus useful for the EEEM030 module. The pattern recognition skills gained from EEE3032 would be useful for understanding the use of pattern recognition algorithms to speech data for achieving speech and speaker recognition.  

Medical robotics is a rapidly developing industry that is vital to healthcare systems seeking better outcomes at reduced overall cost. This module introduces students to the workings of robots and how this is applied in solving both surgical and broader healthcare challenges. Students learn through lectures, practical sessions, tutorials and seminars requiring engagement with scientific literature. Case studies are a critical part of the module. These are industrially or research-based and require students to think about clinical conditions, ethical considerations and a range of attitudes to healthcare.The unit of assessment requires students to develop an understanding of the general theory behind robotics and identify the particularities applying to medical robots. The unit of assessment will also address the wider issues associated with medical robotics and their application. Practical sessions enable students to work with each other to solve problems and demonstrate the theoretical components of the module. An overarching series of lectures delivers critical technical information and links students¿ learning with case-studies to illustrate mechanical and medical issues. Seminars complement lectures and are designed for students to critically analyse, discuss and focus on particular aspects of medical robotic research in the scientific literature.

Module purpose: Advances related to energy efficiency issues and cost reductions have resulted in the rapid growth and deployment of networked devices and sensing/actuation systems that connect the physical world with the cyber world. This evolving framework, now recognized as the Artificial Intelligence of Things (AIoT), integrates IoT technologies with artificial intelligence (AI) to enhance automation, intelligence, and decision-making. AIoT incorporates several cutting-edge technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, distributed systems, and machine learning-driven analytics. Module Overview: The advanced AIoT module is designed to provide a comprehensive understanding of how machine communications , coupled with AI, contribute to creating smart artificial intelligence-driven environments, focusing on networking and communication systems. The module provides an overview of the key concepts and enabling technologies for AIoT. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for AIoT across physical, media access, and network layers. This includes security considerations, satellite AIoT, positioning and tracking for industrial applications, AIoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module explores AI-driven methodologies such as ensemble learning, multi-armed bandits for sequential decision-making, and lightweight machine learning Mmodels for AIoT applications. These techniques enhance real-time data analysis, predictive maintenance, anomaly detection, and adaptive control while ensuring computational efficiency.

Machine learning and deep learning have emerged as core areas within computer science and artificial intelligence, drawing on methods from statistics, applied mathematics, pattern recognition, and neural network computation. This module provides a comprehensive introduction to the theory and application of advanced machine learning and deep learning techniques.The module explores how these methods are applied across a wide range of domains, including natural language processing, large language models, medical imaging, healthcare, audio and speech processing, computer vision, multimodal large language models, large multimodal models and financial technologies (fintech). Students will gain an understanding of how modern AI systems are designed to process and analyse diverse data types such as images, video, text, and audio.The deep learning algorithms covered in this module are widely used in industry, from AI start-ups to major technology companies such as Google, Meta, Microsoft, Nvidia, Amazon, and Tesla. The module combines theoretical foundations with practical implementation, enabling students to develop, evaluate, and apply machine learning models to real-world problems.  

This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions. 

An introduction to the technology and concepts that underpin the design of intelligent vehicles. The module begins with an introduction to intelligent vehicle technologies and their subsystems, such as sensors, perception, and localisation. It then proceeds to cover the design of intelligent vehicle decision-making systems, which includes mission planning, behavioral planning, and motion planning. The latter part of the course delves into the design and implementation of control and prediction systems for intelligent vehicles. The final lecture introduces advanced topics in intelligent vehicle design.

Expected prior learning:  Module EEE3008–Fundamentals of Digital Signal Processing  or equivalent learning about signal processing. Module purpose:  The module discusses basic concepts, signal processing methods and human computer interaction applications of speech processing and recognition including auditory perception and psychoacoustics. You will be taught how to extract salient features from speech signals, how to design a model of spoken language, how to perform recognition and training, and given an insight into current research on spontaneous speech recognition, such as speaker adaptation and solutions for robustness to noise. Demonstrations, interesting illustrations and working examples will be given. Successful students can either proceed to do PhDs or get jobs in the R & D departments of industry, i.e. jobs that are at a higher level than mere software package operators. The presented techniques have many other applications beyond speech, including expert systems and financial modelling. Module EEEM030 contributes to the development of student’s knowledge in audio and speech processing and recognition, which may be useful for their taking of other modules such as: EEEM071 Advanced Topics in Computer Vision and Deep Learning EEEM004 60 Credit Standard Project EEEM005 AI and AI Programming EEEM066 Fundamentals of Machine Learning EEEM067 AR, VR and Metaverse EEEM068 Applied Machine Learning Module EEEM030 contributes to student knowledge in audio and speech processing and thus useful for students taking 60 credit project (EEEM004) related to audio and speech processing and recognition. EEEM030 is related to EEEM005, EEEM066 and EEEM068 due to the fact that machine learning/AI techniques are used for speech and speaker recognition, therefore, EEEM030 contributes to the development of student knowledge in machine learning/AI, which is beneficial for their taking of machine learning and AI related modules such as EEEM005, EEEM066 and EEEM068. One of the applications of audio and speech processing is to apply it to AR, VR and Metaverse for spatial sound production and reproduction which is a key enabling technology for AR, VR, and Metaverse, for virtual sound reproduction. Therefore, knowledge gained from EEEM030 would be useful for the taking of the module EEEM067. Module EEE030 also benefits from knowledge gained from other modules such as: EEE3008 Digital Signal Processing EEE1033 Computer and Digital Logic EEE1035 Programming  in C EEE3042 Audio and Video Processing EEE3032 Computer Vision and Pattern Recognition The modules EEE1033 and EEE1035 provide students with some useful skills in programming, which will be beneficial for them to complete the computer programming based coursework components, by turning the signal processing theories and methods into working program codes. The module EEE3008 provides students with knowledge and skills on fundamental digital signal processing skills which are essential in understanding the application of these skills to audio and speech data. EEE3042 covers both audio and video processing and coding, and the audio related materials are highly relevant and thus useful for the EEEM030 module. The pattern recognition skills gained from EEE3032 would be useful for understanding the use of pattern recognition algorithms to speech data for achieving speech and speaker recognition.  

Medical robotics is a rapidly developing industry that is vital to healthcare systems seeking better outcomes at reduced overall cost. This module introduces students to the workings of robots and how this is applied in solving both surgical and broader healthcare challenges. Students learn through lectures, practical sessions, tutorials and seminars requiring engagement with scientific literature. Case studies are a critical part of the module. These are industrially or research-based and require students to think about clinical conditions, ethical considerations and a range of attitudes to healthcare.The unit of assessment requires students to develop an understanding of the general theory behind robotics and identify the particularities applying to medical robots. The unit of assessment will also address the wider issues associated with medical robotics and their application. Practical sessions enable students to work with each other to solve problems and demonstrate the theoretical components of the module. An overarching series of lectures delivers critical technical information and links students¿ learning with case-studies to illustrate mechanical and medical issues. Seminars complement lectures and are designed for students to critically analyse, discuss and focus on particular aspects of medical robotic research in the scientific literature.

Module purpose: Advances related to energy efficiency issues and cost reductions have resulted in the rapid growth and deployment of networked devices and sensing/actuation systems that connect the physical world with the cyber world. This evolving framework, now recognized as the Artificial Intelligence of Things (AIoT), integrates IoT technologies with artificial intelligence (AI) to enhance automation, intelligence, and decision-making. AIoT incorporates several cutting-edge technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, distributed systems, and machine learning-driven analytics. Module Overview: The advanced AIoT module is designed to provide a comprehensive understanding of how machine communications , coupled with AI, contribute to creating smart artificial intelligence-driven environments, focusing on networking and communication systems. The module provides an overview of the key concepts and enabling technologies for AIoT. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for AIoT across physical, media access, and network layers. This includes security considerations, satellite AIoT, positioning and tracking for industrial applications, AIoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module explores AI-driven methodologies such as ensemble learning, multi-armed bandits for sequential decision-making, and lightweight machine learning Mmodels for AIoT applications. These techniques enhance real-time data analysis, predictive maintenance, anomaly detection, and adaptive control while ensuring computational efficiency.

Machine learning and deep learning have emerged as core areas within computer science and artificial intelligence, drawing on methods from statistics, applied mathematics, pattern recognition, and neural network computation. This module provides a comprehensive introduction to the theory and application of advanced machine learning and deep learning techniques.The module explores how these methods are applied across a wide range of domains, including natural language processing, large language models, medical imaging, healthcare, audio and speech processing, computer vision, multimodal large language models, large multimodal models and financial technologies (fintech). Students will gain an understanding of how modern AI systems are designed to process and analyse diverse data types such as images, video, text, and audio.The deep learning algorithms covered in this module are widely used in industry, from AI start-ups to major technology companies such as Google, Meta, Microsoft, Nvidia, Amazon, and Tesla. The module combines theoretical foundations with practical implementation, enabling students to develop, evaluate, and apply machine learning models to real-world problems.  

This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions. 

Expected prior learning:  Module EEE3008–Fundamentals of Digital Signal Processing  or equivalent learning about signal processing. Module purpose:  The module discusses basic concepts, signal processing methods and human computer interaction applications of speech processing and recognition including auditory perception and psychoacoustics. You will be taught how to extract salient features from speech signals, how to design a model of spoken language, how to perform recognition and training, and given an insight into current research on spontaneous speech recognition, such as speaker adaptation and solutions for robustness to noise. Demonstrations, interesting illustrations and working examples will be given. Successful students can either proceed to do PhDs or get jobs in the R & D departments of industry, i.e. jobs that are at a higher level than mere software package operators. The presented techniques have many other applications beyond speech, including expert systems and financial modelling. Module EEEM030 contributes to the development of student’s knowledge in audio and speech processing and recognition, which may be useful for their taking of other modules such as: EEEM071 Advanced Topics in Computer Vision and Deep Learning EEEM004 60 Credit Standard Project EEEM005 AI and AI Programming EEEM066 Fundamentals of Machine Learning EEEM067 AR, VR and Metaverse EEEM068 Applied Machine Learning Module EEEM030 contributes to student knowledge in audio and speech processing and thus useful for students taking 60 credit project (EEEM004) related to audio and speech processing and recognition. EEEM030 is related to EEEM005, EEEM066 and EEEM068 due to the fact that machine learning/AI techniques are used for speech and speaker recognition, therefore, EEEM030 contributes to the development of student knowledge in machine learning/AI, which is beneficial for their taking of machine learning and AI related modules such as EEEM005, EEEM066 and EEEM068. One of the applications of audio and speech processing is to apply it to AR, VR and Metaverse for spatial sound production and reproduction which is a key enabling technology for AR, VR, and Metaverse, for virtual sound reproduction. Therefore, knowledge gained from EEEM030 would be useful for the taking of the module EEEM067. Module EEE030 also benefits from knowledge gained from other modules such as: EEE3008 Digital Signal Processing EEE1033 Computer and Digital Logic EEE1035 Programming  in C EEE3042 Audio and Video Processing EEE3032 Computer Vision and Pattern Recognition The modules EEE1033 and EEE1035 provide students with some useful skills in programming, which will be beneficial for them to complete the computer programming based coursework components, by turning the signal processing theories and methods into working program codes. The module EEE3008 provides students with knowledge and skills on fundamental digital signal processing skills which are essential in understanding the application of these skills to audio and speech data. EEE3042 covers both audio and video processing and coding, and the audio related materials are highly relevant and thus useful for the EEEM030 module. The pattern recognition skills gained from EEE3032 would be useful for understanding the use of pattern recognition algorithms to speech data for achieving speech and speaker recognition.  

Medical robotics is a rapidly developing industry that is vital to healthcare systems seeking better outcomes at reduced overall cost. This module introduces students to the workings of robots and how this is applied in solving both surgical and broader healthcare challenges. Students learn through lectures, practical sessions, tutorials and seminars requiring engagement with scientific literature. Case studies are a critical part of the module. These are industrially or research-based and require students to think about clinical conditions, ethical considerations and a range of attitudes to healthcare.The unit of assessment requires students to develop an understanding of the general theory behind robotics and identify the particularities applying to medical robots. The unit of assessment will also address the wider issues associated with medical robotics and their application. Practical sessions enable students to work with each other to solve problems and demonstrate the theoretical components of the module. An overarching series of lectures delivers critical technical information and links students¿ learning with case-studies to illustrate mechanical and medical issues. Seminars complement lectures and are designed for students to critically analyse, discuss and focus on particular aspects of medical robotic research in the scientific literature.

Module purpose: Advances related to energy efficiency issues and cost reductions have resulted in the rapid growth and deployment of networked devices and sensing/actuation systems that connect the physical world with the cyber world. This evolving framework, now recognized as the Artificial Intelligence of Things (AIoT), integrates IoT technologies with artificial intelligence (AI) to enhance automation, intelligence, and decision-making. AIoT incorporates several cutting-edge technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, distributed systems, and machine learning-driven analytics. Module Overview: The advanced AIoT module is designed to provide a comprehensive understanding of how machine communications , coupled with AI, contribute to creating smart artificial intelligence-driven environments, focusing on networking and communication systems. The module provides an overview of the key concepts and enabling technologies for AIoT. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for AIoT across physical, media access, and network layers. This includes security considerations, satellite AIoT, positioning and tracking for industrial applications, AIoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module explores AI-driven methodologies such as ensemble learning, multi-armed bandits for sequential decision-making, and lightweight machine learning Mmodels for AIoT applications. These techniques enhance real-time data analysis, predictive maintenance, anomaly detection, and adaptive control while ensuring computational efficiency.

Machine learning and deep learning have emerged as core areas within computer science and artificial intelligence, drawing on methods from statistics, applied mathematics, pattern recognition, and neural network computation. This module provides a comprehensive introduction to the theory and application of advanced machine learning and deep learning techniques.The module explores how these methods are applied across a wide range of domains, including natural language processing, large language models, medical imaging, healthcare, audio and speech processing, computer vision, multimodal large language models, large multimodal models and financial technologies (fintech). Students will gain an understanding of how modern AI systems are designed to process and analyse diverse data types such as images, video, text, and audio.The deep learning algorithms covered in this module are widely used in industry, from AI start-ups to major technology companies such as Google, Meta, Microsoft, Nvidia, Amazon, and Tesla. The module combines theoretical foundations with practical implementation, enabling students to develop, evaluate, and apply machine learning models to real-world problems.  

This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions. 

An introduction to the technology and concepts that underpin the design of intelligent vehicles. The module begins with an introduction to intelligent vehicle technologies and their subsystems, such as sensors, perception, and localisation. It then proceeds to cover the design of intelligent vehicle decision-making systems, which includes mission planning, behavioral planning, and motion planning. The latter part of the course delves into the design and implementation of control and prediction systems for intelligent vehicles. The final lecture introduces advanced topics in intelligent vehicle design.

Expected prior learning:  Module EEE3008–Fundamentals of Digital Signal Processing  or equivalent learning about signal processing. Module purpose:  The module discusses basic concepts, signal processing methods and human computer interaction applications of speech processing and recognition including auditory perception and psychoacoustics. You will be taught how to extract salient features from speech signals, how to design a model of spoken language, how to perform recognition and training, and given an insight into current research on spontaneous speech recognition, such as speaker adaptation and solutions for robustness to noise. Demonstrations, interesting illustrations and working examples will be given. Successful students can either proceed to do PhDs or get jobs in the R & D departments of industry, i.e. jobs that are at a higher level than mere software package operators. The presented techniques have many other applications beyond speech, including expert systems and financial modelling. Module EEEM030 contributes to the development of student’s knowledge in audio and speech processing and recognition, which may be useful for their taking of other modules such as: EEEM071 Advanced Topics in Computer Vision and Deep Learning EEEM004 60 Credit Standard Project EEEM005 AI and AI Programming EEEM066 Fundamentals of Machine Learning EEEM067 AR, VR and Metaverse EEEM068 Applied Machine Learning Module EEEM030 contributes to student knowledge in audio and speech processing and thus useful for students taking 60 credit project (EEEM004) related to audio and speech processing and recognition. EEEM030 is related to EEEM005, EEEM066 and EEEM068 due to the fact that machine learning/AI techniques are used for speech and speaker recognition, therefore, EEEM030 contributes to the development of student knowledge in machine learning/AI, which is beneficial for their taking of machine learning and AI related modules such as EEEM005, EEEM066 and EEEM068. One of the applications of audio and speech processing is to apply it to AR, VR and Metaverse for spatial sound production and reproduction which is a key enabling technology for AR, VR, and Metaverse, for virtual sound reproduction. Therefore, knowledge gained from EEEM030 would be useful for the taking of the module EEEM067. Module EEE030 also benefits from knowledge gained from other modules such as: EEE3008 Digital Signal Processing EEE1033 Computer and Digital Logic EEE1035 Programming  in C EEE3042 Audio and Video Processing EEE3032 Computer Vision and Pattern Recognition The modules EEE1033 and EEE1035 provide students with some useful skills in programming, which will be beneficial for them to complete the computer programming based coursework components, by turning the signal processing theories and methods into working program codes. The module EEE3008 provides students with knowledge and skills on fundamental digital signal processing skills which are essential in understanding the application of these skills to audio and speech data. EEE3042 covers both audio and video processing and coding, and the audio related materials are highly relevant and thus useful for the EEEM030 module. The pattern recognition skills gained from EEE3032 would be useful for understanding the use of pattern recognition algorithms to speech data for achieving speech and speaker recognition.  

Medical robotics is a rapidly developing industry that is vital to healthcare systems seeking better outcomes at reduced overall cost. This module introduces students to the workings of robots and how this is applied in solving both surgical and broader healthcare challenges. Students learn through lectures, practical sessions, tutorials and seminars requiring engagement with scientific literature. Case studies are a critical part of the module. These are industrially or research-based and require students to think about clinical conditions, ethical considerations and a range of attitudes to healthcare.The unit of assessment requires students to develop an understanding of the general theory behind robotics and identify the particularities applying to medical robots. The unit of assessment will also address the wider issues associated with medical robotics and their application. Practical sessions enable students to work with each other to solve problems and demonstrate the theoretical components of the module. An overarching series of lectures delivers critical technical information and links students¿ learning with case-studies to illustrate mechanical and medical issues. Seminars complement lectures and are designed for students to critically analyse, discuss and focus on particular aspects of medical robotic research in the scientific literature.

Module purpose: Advances related to energy efficiency issues and cost reductions have resulted in the rapid growth and deployment of networked devices and sensing/actuation systems that connect the physical world with the cyber world. This evolving framework, now recognized as the Artificial Intelligence of Things (AIoT), integrates IoT technologies with artificial intelligence (AI) to enhance automation, intelligence, and decision-making. AIoT incorporates several cutting-edge technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, distributed systems, and machine learning-driven analytics. Module Overview: The advanced AIoT module is designed to provide a comprehensive understanding of how machine communications , coupled with AI, contribute to creating smart artificial intelligence-driven environments, focusing on networking and communication systems. The module provides an overview of the key concepts and enabling technologies for AIoT. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for AIoT across physical, media access, and network layers. This includes security considerations, satellite AIoT, positioning and tracking for industrial applications, AIoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module explores AI-driven methodologies such as ensemble learning, multi-armed bandits for sequential decision-making, and lightweight machine learning Mmodels for AIoT applications. These techniques enhance real-time data analysis, predictive maintenance, anomaly detection, and adaptive control while ensuring computational efficiency.

Machine learning and deep learning have emerged as core areas within computer science and artificial intelligence, drawing on methods from statistics, applied mathematics, pattern recognition, and neural network computation. This module provides a comprehensive introduction to the theory and application of advanced machine learning and deep learning techniques.The module explores how these methods are applied across a wide range of domains, including natural language processing, large language models, medical imaging, healthcare, audio and speech processing, computer vision, multimodal large language models, large multimodal models and financial technologies (fintech). Students will gain an understanding of how modern AI systems are designed to process and analyse diverse data types such as images, video, text, and audio.The deep learning algorithms covered in this module are widely used in industry, from AI start-ups to major technology companies such as Google, Meta, Microsoft, Nvidia, Amazon, and Tesla. The module combines theoretical foundations with practical implementation, enabling students to develop, evaluate, and apply machine learning models to real-world problems.  

This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions.