11312 modules
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ELEC6262 2026-27
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
ELEC6262 2030-31
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
ELEC3228 2028-29
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
ELEC6262 2029-30
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
ELEC6262 2027-28
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
LING1002 2026-27
From English to Englishes
This module introduces you to the spread of English from its historical origins to colonial and postcolonial contexts and, further, to current global contexts. It explores sociolinguistic issues and debates centering on the development of English. It engages with different approaches to the understanding of English - for example, World Englishes, Global Englishes and English as a lingua franca - in local and global contexts. -
LING1002 2025-26
From English to Englishes
This module introduces you to the spread of English from its historical origins to colonial and postcolonial contexts and, further, to current global contexts. It explores sociolinguistic issues and debates centering on the development of English. It engages with different approaches to the understanding of English - for example, World Englishes, Global Englishes and English as a lingua franca - in local and global contexts. -
LING1002 2027-28
From English to Englishes
This module introduces you to the spread of English from its historical origins to colonial and postcolonial contexts and, further, to current global contexts. It explores sociolinguistic issues and debates centering on the development of English. It engages with different approaches to the understanding of English - for example, World Englishes, Global Englishes and English as a lingua franca - in local and global contexts. -
BIOM1011 2027-28
From Molecules to Life
This module is designed for Biomedical Engineering students to extend their core biomedical science understanding while developing sound foundations in electromagnetism and in digital health, relevant to biomedical engineering.
In this module, you will learn about the different types of macromolecules found in cells, with a particular emphasis on DNA and proteins. The module will study the role of nucleic acids and genetic information in defining protein structure, how protein structure influences function, and the principles by which DNA and protein can be manipulated by molecular biology.
This module will also introduce the methods used to collect, present, and analyse data in the life sciences and to estimate models from data, with focused application to introductory level statistical genetics. These topics will provide a grounding in the principles underlying modern biological data science.
This module will also introduce the physical concepts of fields and waves plus the behaviour of materials in electric and magnetic fields, to provide a solid foundation in the physics underpinning biomedical technology. -
BIOM1011 2026-27
From Molecules to Life
This module is designed for Biomedical Engineering students to extend their core biomedical science understanding while developing sound foundations in electromagnetism and in digital health, relevant to biomedical engineering.
In this module, you will learn about the different types of macromolecules found in cells, with a particular emphasis on DNA and proteins. The module will study the role of nucleic acids and genetic information in defining protein structure, how protein structure influences function, and the principles by which DNA and protein can be manipulated by molecular biology.
This module will also introduce the methods used to collect, present, and analyse data in the life sciences and to estimate models from data, with focused application to introductory level statistical genetics. These topics will provide a grounding in the principles underlying modern biological data science.
This module will also introduce the physical concepts of fields and waves plus the behaviour of materials in electric and magnetic fields, to provide a solid foundation in the physics underpinning biomedical technology.