Written assignment

1 - Written assignment (including essay) - 30

Examination

2 - Written exam - 70

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<ol><li>Fundamentals of Artificial Intelligence and Machine Learning. </li><li>Data </li><li>Classification and regression </li><li>Machine learning techniques, training, testing and coding </li><li>Bayesian approaches. </li><li>Perceptrons, multilayer networks and backpropagation. </li><li>Implementation of Artificial Neural Networks. </li><li>Genetic algorithms. </li></ol>

Feedback is provided in a number of ways throughout your module. This includes feedback you receive during teaching from both peers and lecturers and that provided for formative assessment. Following an assessment, you will be given your marks and feedback in line with the assessment regulations and policy. More information on the timing and type of feedback that will be provided for each assessment will be available from the module leader. <a href='https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations'>https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations</a>

This module will support the development of all the graduate attributes. Your module leader will provide you with information about how your learning, teaching and assessment activities support your development of these attributes. The graduate attributes are: - Always learning - Engaged in the world - Technical and digital - Connected Professional - Creating Impact

Self-directed - 

In-Person - 

Artificial Intelligence A Modern Approach

Pattern Recognition and Machine Learning

Deep  Learning

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

INM466 - Principles of  Artificial Intelligence

INM466-PRD2-PER-ALV1

The key aspects of the module are introduced in lectures together with examples. You will apply the lecture material in laboratory sessions that combine practical exercises and theoretical questions. Materials for each session will be provided via the Moodle learning environment. You are expected to continue to study in your own time by completing exercises, checking your efforts against solutions that are released subsequently and reading literature that is introduced in class and online. The reading list is a key resource that should form the basis of independent study.

Principles of  Artificial Intelligence

Apply knowledge of Artificial Intelligence to solve machine learning problems. 

Knowledge

Appreciate privacy aspects and evaluate ethical issues when working with artificial intelligence. 

Values and attitudes

Discuss concisely the main theoretical and practical issues involved in designing Artificial Intelligence systems and solutions. 

Construct nature-inspired Artificial Intelligence architectures. 

Skills

Evaluate recent advances in adaptive Artificial Intelligence. 

Plan and organise the use of Artificial Intelligence algorithms taking advantage of concepts and theories specific to error-correction learning and evolutionary computation. 

Formulate and design appropriate application of Artificial Intelligence algorithms to synthesise solutions, from initial goals to critical evaluation and testing, including data acquisition and preparation. 