Design and Professional Skills II (CEGE0088)

MACHINE LEARNING|DEEP LEARNING|REINFORCMENT LEARNING| ARTIFICIAL INTELLIGENCE

This module covers principles of machine learning (ML) and a general overview of AI technology and terminology. This module will make students gain intuitions...

Machine Learning for Data Science (CEGE0004)

This module covers principles of machine learning (ML) and a general overview of AI technology and terminology. This module will make students gain intuitions about a number of ML algorithms and make them comfortable in applying them to new data and tasks. Also, students will gain experience in developing (in python) ML-based solutions to real world problems.

Learning outcomes:

To understand the basic principles of machine learning;
 To be comfortable in applying appropriate machine learning methods to new data and tasks;
 To understand the fundamentals of algorithms in machine learning;
 To understand the fundamentals of algorithms for deep learning;
 To have a general overview of the current AI technology and terminology.

This is the report module for the MA Digital Media: Education route. All students undertake an independent research project based on topics and materials...

Report on Digital Media: Education (CCME0061)

This is the report module for the MA Digital Media: Education route. All students undertake an independent research project based on topics and materials covered on the Education route, which culminates in either a dissertation of 20,000 words or a report of 10,000 words.  The choice of a Dissertation or a Report is a compulsory component of  the MA Digital Media: Education programme.    

Artificial Intelligence|Computer Science|Information Systems|Quantitative Analysis

Students will learn the main concepts of deep learning, understand how to apply deep learning to data streams from cameras and other IoT sensors, and learn how...

Deep Learning for Sensor Networks (CASA0018)

Students will learn the main concepts of deep learning, understand how to apply deep learning to data streams from cameras and other IoT sensors, and learn how to structure successful deep learning projects. Students will learn about deep learning architectures such as fully connected networks, convolutional networks and RNNs. Students will apply these ideas to sensor data in order to do forecasting, image recognition, and object tracking. Students will master not only the basic theory, but also learn how to diagnose errors and prioritise directions in deep learning projects. Students will practice all these ideas in Python and TensorFlow.


Aim: To give the students the understanding and practical experience of applying deep learning to sensor data (video, time series), and develop the skill set to design and implement deep learning systems for IoT devices.