All students undertake a substantial research project working in association with one of the research groups at UCL or a collaborating industrial research...

All students undertake a substantial research project working in association with one of the research groups at UCL or a collaborating industrial research laboratory, culminating in a dissertation.

The Professional Learning Portfolio is the first of two modules (the other being Understanding Teaching) that make up the School Direct Salaried PGCE Top Up...

Aims:

During the Secondary PGCE, you will undertake two sustained School Placements in contrasting schools. During this time you are expected to complete a...

Secondary - Professional Practice (CPAS0003)

Aims:

During the Secondary PGCE, you will undertake two sustained School Placements in contrasting schools. During this time you are expected to complete a total of up to 120 days (subject to DfE guidelines) in secondary schools or other approved educational settings.

You will:

Develop classroom teaching skills;
	Develop the knowledge and understanding that underpin these skills;
Develop subject knowledge;
	Develop the ability to work with others in a collaborative and professional way.
Outcomes:

You will demonstrate an understanding of the QTS (Qualified Teacher Status) standards through a range of evidence as listed below;

An online Assessment and Development Portfolio (ADP), including formal lesson observations, mentor meeting minutes, tutor visit summaries, periodic formal reflections and end of placement reports;
	Practical Teaching Files;
	Lesson Plans;
	Evidence of Professional Development;
	Evidence relating to your professional attributes including relationships between you, the staff and pupils;
	Punctuality and attendance

Aims:

In this module students will learn the fundamental concepts and techniques of AI and how they are applied to solve complex real-word problems. The...

Aims:

In this module students will learn the fundamental concepts and techniques of AI and how they are applied to solve complex real-word problems. The course will equip students with knowledge and tools to tackle new AI problems in different fields. 

Provide a broad introduction to the different techniques used in AI and their range of applicability. 
	Enable students to propose and design AI solutions to a range of domain specific applications. 
	Enable students to be effective team players in interdisciplinary research groups developing and applying AI systems.
Learning outcomes:

On successful completion of the module, a student will be able to: 

Demonstrate an understanding of the fundamental concepts in AI. 
Demonstrate an understanding of the classical and modern approaches to AI. 
Recognize real-word applications where AI modelling can be applied. 
Implement and critically evaluate current state-of-the art AI approaches for a range of domain specific applications. 
Content:

Introduction to AI approaches and the deployment of applications to solve complex real-word problems

Theory and practice of classical AI techniques covering problem representation, search-based AI, knowledge representation and logic-based information technologies, as well as more novel reasoning and planning strategies,

Introduction to hybrids of classical AI with modern machine learning such as symbolic neural networks.

Requisites:

To be eligible to select this module as an optional or elective, a student must be registered on a programme and year of study for which it is formally available.