Course programme
What you'll study You will study the following core modules: Module title Credits. Foundations of Neuroscience Foundations of Neuroscience 15 credits This module covers brain anatomy and functions and modern experimental techniques to study the neural basis of behaviour. Tutors: Professor Joydeep Bhattacharya and Dr Gianna Cocchini 15 credits. Statistical Methods Statistical Methods 15 credits This module covers primary statistical analyses used in psychology and neuroscience. This includes: multivariate data screening and cleaning. power and sample size determination. factor analysis. multiple regression. analysing contrasts. univariate and multivariate repeated measures. psychometrics.. Tutor: Dr Devin Terhune.. 15 credits. Cortical Modelling Cortical Modelling 15 credits This module covers the theory and practice of computational neuroscience, including computational models of neurons, synapses, simple cortical circuits and networks. Students will learn how to implement simple models of biologically-realistic neural systems. 15 credits. Cognitive Neuroscience Cognitive Neuroscience 15 credits This module covers the current state of knowledge in the field of cognitive neuroscience. It covers lower-level, fundamental cognitive processes, such as perception, attention, action, vision, audition, and motor control, as well as higher functions such as memory, speech, language, executive functions and cognitive control. Tutor: Dr. Maria Herrojo Ruiz.. 15 credits. Modelling Cognitive Processes and Higher Order Brain Functions Modelling Cognitive Processes and Higher Order Brain Functions 15 credits This module covers the fundamental principles of current computational models of cognitive functions and their emergence. This includes perception and attention, working and long-term memory, decision making, and language. Tutor: Dr Max Garagnani 15 credits. Advanced Quantitative Methods Advanced Quantitative Methods 15 credits The theory and practice of advanced quantitative methods across multiple areas of psychology and neuroscience. Tutor: Dr. Maria Herrojo Ruiz 15 credits. You will also undertake a 60 credit research project investigating an aspect of cognitive neuroscience using computational modelling, advanced data analysis methods, or a combination of these techniques. Culminating in a 10,000 word dissertation, the project will be carried out by combining the computational, experimental and data analysis skills that students will acquire over Term 1 and 2. Option modules You will choose one option from the following two modules: Module title Credits. Data Programming Data Programming 15 credits This module introduces programming for Data Science, concentrating primarily on the tools and techniques that are key to achieve results quickly. The module covers current programming languages and environments commonly used in the wider Data Science community, along with ancillary tools and software systems, and gives the student the foundational skills to allow them to develop data-related software for their specific areas of interest. The knowledge and skills in this course cover the following general areas: Data representations: basic data types, comma-separated variables, Xtensible Markup Language, JavaScript Object Notation, Resource Description Framework, Relations; Data acquisition, storage, retrieval and publication: filesystems, version control, network programming, HTTP, Web servers, relational database systems; Data programming: string processing, numeric vector processing, data frames, scripting and statistical programming; Visualizations: automatically generating charts, graphs, and choropleth maps. Tutor: Dr Sorrel Harriet 15 credits. Introduction to coding with MATLAB Introduction to coding with MATLAB 15 credits This module aims to provide students with a comprehensive introduction to MATLAB, a widely-used software package for data analysis. The module will start in the Autumn term of 2018 and will be suitable for third year undergraduate students in psychology as well as for students in other programmes. Each 1 hour lecture will introduce the topic, associated functions, and theory and will be followed by a 2 hour lab session with hands-on training and exercises using MATLAB. Weekly homework will help to further consolidate the material. Tutors: Devin B Terhune and Maria Herrojo Ruiz 15 credits. You will also choose one of the following 4 options: Module title Credits. Neural Networks Neural Networks 15 credits Introduces the theory and practice of neural computation. Covers the principles of neurocomputing with artificial neural networks widely used for addressing real-world problems such as classification, regression, pattern recognition, data mining, time-series prediction. We look at supervised and unsupervised learning. We study supervised learning using linear perceptrons, and non-linear models such as probabilistic neural networks, multilayer perceptrons, and radial-basis function networks. Unsupervised learning is studied using Kohonen networks. We provide contemporary training techniques for all these neural networks, and knowledge and tools for the specification, design, and practical implementation of neural networks. Tutor: Dr Nikolay Nikolaev 15 credits. Machine Learning Machine Learning 15 credits This course provides a broad introduction to machine learning and statistical pattern recognition. The very general topics will include supervised learning (generative/discriminative learning, parametric/non-parametric learning), unsupervised learning (clustering, dimensionality reduction), and learning theory (bias/variance tradeoffs). The course will also discuss recent applications of machine learning (e.g., in computer vision, or other applications relevant to the research orientation of the department of computing). Tutor: Dr Mihalis Nicolaou 15 credits. Natural Computing Natural Computing 15 credits Throughout history, nature has been a source of inspiration for scientists and researchers. Observations, many made accidentally, have been triggering inquisitive minds for centuries. In this module, students will be introduced to various concepts in nature to build an understanding of nature-inspired swarm intelligence and evolutionary computation techniques. Students are then guided through the process of implementation of adaptation of these techniques to apply to various existing real-world problems (e.g. clustering, medical imaging, optimisation and visualisation). Tutor: Dr Mohammad Majid Al-Rifaie 15 credits. Research Design and Analysis Research Design and Analysis 15 credits The aim of this module is to provide understanding and skills related to research design and to provide extra support for design aspects of dissertation work. Topics include: basic concepts; non-experimental methods; experimental methods; quasi-experimental methods; ethical considerations; experience using online databases and other resources; a seminar on design and statistics as principled argument. Tutor: Dr Karina Linnell 15 credits. Download the programme specification for the 2018-19 intake. If you would like an earlier version of the programme specification, please contact the Quality Office. Please note that due to staff research commitments not all of these modules may be available every year.
