Departments of Chemistry and Mathematics MSc Chemoinformatics

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0580050 Statistics and Probability Theory Autumn term, 10 Credits Module Organiser: Garib Murshudov (Chemistry), Room: B/K/065, Tel: 8253, e-mail: garib@ysbl.york.ac.uk Aims: Information flow and exchange is an integrated part of the modern life. Size and amount of data sets are ever increasing. Data analysis is an important part of understanding a variety of data types. This course aims to introduce the most popular data analytical techniques and their application to students using one of the available packages (examples are given using the package R). Learning objectives: At the end of the course students should be aware of the role of statistics and be familiar with a number of practical techniques from data analysis. Syllabus: In the first part of the course, basic statistical terms and concepts are introduced. In the second part, the most widely used data analysis techniques are described and illustrated. Basic Introduction to R: this lecture introduces basic commands from the statistical package R. Role and place of data analysis: this gives a basic introduction to data analysis. Maximum likelihood: maximum likelihood and related concepts such as observed and Fisher information are described. Elementary hypothesis testing: the idea behind hypothesis testing is explored. Re-sampling techniques: some of the techniques for bias reduction and prediction error estimation are described. These include jackknife, bootstrap, cross-validation. Linear and generalised linear model: classic linear model for normal distribution case and its generalisation for the natural exponential family is described. Fixed, random and mixed effects models Elements of design of experiment Basics of ANOVA: purpose and ways of analysing ANOVA tables are described. Log-linear and logistic models: cases when data are from Poisson and binomial distribution are described. Teaching: Lectures: 20 x 2 hr lectures. Practicals: 10 x 1 hr practicals. Private study: 43 hrs. Assessment: 7 hrs. Assessment: An all day open (computing) exam in week 10 of the Autumn term (90%). Coursework (10%). Recommended texts: Intelligent data Analysis, Berthold M. and Hand D.J. Introductory Statistics with R, Dalgaard P. Experimental Design, Cochran W.G. and Cox G.M. Prerequisites: None. 0780170 Introduction to Signal Processing Autumn term, 10 Credits
Module Organiser: Yuriy Zakharov (Electronics), Room: P/K/001, Tel: 2399, e-mail: yz1@ohm.york.ac.uk Aims: This module introduces the students to the fundamental concepts of signal processing:analog and digital signals and systems, Fourier series, sampling, statistical signal processing and parameter estimation. Learning objectives: At the end of this module students are expected to: Understand signal sampling and reconstruction Analyse continuous and discrete-time signals and systems in the time and frequency domain Understand cocepts of autocorrelation, convolution and linearity Understand statistical properties of signals Understand principles of parameter estimation in noise Syllabus: Analogue and digital signals Signal sampling and reconstruction The sampling theorem and Nyquist interval Random processes, probability density function, correlation and spectral density Systems, linearity and time-invariance Impulse and frequency responses Convolution, Fourier series, Discrete Forier Transform (DFT) and Fast Fourier Transform (FFT) Fundamentals of linear parameter estimation and spectrum estimation Least squares and maximum liklihood estimates Teaching: Teaching: Lectures: 10 x 2 hr lectures. Practicals: 2 x 1 hr practicals. Private study: 75.5 hrs. Assessment: 2.5 hrs. Students will receive handouts, tutorial questions and revision questions. Recommended texts: Lathi, B.P. "Signal Processing and Linear Systems", 2003, Oxford University Press, ISBN 0195219171. Kay, S.M. "Fundamentals of Statistical Signal Processing: Estimation Theory", Prentice Hall, 1993. Assessment: This module is assessed by a 2 1/2 hour closed book examination in January. Prerequisites: Knowledge of simple probability theory and matrix algebra. 0680102 Introduction to Programming (PYTHON) Autumn term, 10 Credits
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Departments of Chemistry and Mathematics MSc Chemoinformatics

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