This module will provide you with the essential programming skills, within a chemistry context, to enable you to tackle a broad range of data-intensive chemistry problems and research challenges.

This module is designed in a way to empower you with skills to plan, design and formulate a simulation that realises a truly predictive capability.

The aim of this module is to provide you with the foundations of mathematics that are required to understand modern Artificial Intelligence techniques. The module will focus on three topics: probability, statistics and linear algebra.

On completion of the module you'll be able to:

• understand and apply probability theory as used in modern AI:
• randomness
• probability distributions
• variance and expectation
• expected value
• understand and apply statistical techniques as used in modern AI:
• basic data analysis
• significance tests
• Bayesian inference
• understand and use linear algebra techniques as used in modern AI:
• scalars
• vectors
• matrices
• tensors

This module aims to give you an understanding of the challenges posed by big data, an understanding of the key algorithms and techniques which are embodied in data analytics, and exposure to a number of different big data technologies and techniques.

After completing this module, you'll be able to:

• understand the fundamentals of Python to enable the use of various big data technologies
• understand how classical statistical techniques are applied in modern data analysis
• understand the potential application of data analysis tools for various problems and appreciate their limitations
• be familiar with a number of different cloud NoSQL systems and their design and implementation, showing how they can achieve efficiency and scalability while also addressing design trade-offs and their impact

This module will provide you with the skills necessary to develop, run and evaluate artificial intelligence and machine learning algorithms that can solve a range of problems within a chemistry context. The specific focus will be on data-intensive chemistry problems and research challenges.

This module will provide you with the essential professional software management skills, within a chemistry context, to enable you to create software that is parallelizable and applicable to data-intensive chemistry problems and research challenges.

This module will provide you with a gateway to deploying your advanced Python programming skills to a host of chemistry applications in which monitoring over time is crucial. For chemistry problems on scales from seconds to centuries, you will leave this module with the skill and awareness of how to acquire, transform, and apply time series data in a way that is relevant to advanced, real-world projects in the realms of chemical reaction monitoring.

The aim of this module is to give you an understanding of both quantitative and qualitative research processes and associated techniques, including the effective presentation of findings in accordance with the best principles of scholarship.

The most impactful area of AI has been machine learning using neural networks. When combined with reinforcement learning, these neural networks can also become autonomous agents that can, for example, learn to play games to an extraordinarily high standard. This module will cover these two areas of AI.

After completing this module, you'll be able to:

• define and understand the problem of agents that learn
• understand how Deep Reinforcement Learning uses Deep Neural Networks together with Reinforcement Learning in, for example, the Atari Games work of Google Deepmind

You will conduct an extended piece of research in data science applied to chemistry. This will be carried out in partnership with our expert researchers, and you will be embedded into their research group.