MSc Artificial Intelligence

Module 1: Machine Learning and Data Mining (30 credits)

The full data mining lifecycle, from data preprocessing to exploratory analysis
Supervised and unsupervised algorithms: association rules, clustering and classification
Neural networks, ensemble methods and text mining
Practical activities in Python to extract and interpret insights from data
Ethical and societal implications of machine learning practice

Module 2: Big Data Tools and Techniques (30 credits)

Defining characteristics, challenges and opportunities of Big Data
Cloud computing, distributed processing frameworks and NoSQL databases
Ingesting, processing, analysing and storing massive datasets
Hands-on Apache Spark, PySpark and MongoDB on real-world text, image and IoT sensor data
Machine learning and recommender systems at scale

Module 3: Deep Learning (30 credits)

Deep learning architectures behind speech and face recognition, sentiment analysis and medical diagnosis
Feed-forward and convolutional neural networks, and transfer learning
Sequence models: LSTMs, GRUs and Transformers
Embeddings (Word2Vec, BERT, GPT) and image segmentation
Practical development in Python with TensorFlow and Keras

Module 4: Natural Language Processing (30 credits)

Methods and tools behind chatbots, speech recognition and translation systems
Tokenisation, parsing, and lexical and distributional semantics
Text classification, sentiment analysis, information retrieval and extraction
Neural architectures for NLP, word embeddings and pre-trained models
Critical examination of bias in language models

Module 5: Project for MSc (Dissertation) (60 credits)

Independent, research-informed project on a substantive problem in your field
Robust research proposal and critical review of current practice and the state of the art
Ethical considerations and appropriate methodologies and technologies
A 15,000-word dissertation with results, reflections and recommendations
Supported throughout by a dedicated supervisor