Course pages 2016–17
Machine Learning and Real-world Data
Principal lecturers: Dr Simone Teufel, Prof Ann Copestake
Taken by: Part IA CST 75%
Past exam questions
No. of lectures and practical classes: 16
Suggested hours of supervisions: 4
Prerequisite courses: NST Mathematics
Aims
This course introduces students to machine learning algorithms as used in real-world applications, and to the experimental methodology necessary to perform statistical processing of large-scale unpredictable processes such as language, social networks or genetic data. Students will perform 3 extended practicals, as follows:
- Statistical classification: Determining a movie review’s sentiment using Naive Bayes (7 sessions)
- Sequence Analysis: Detection of proteins in genetic data using Hidden Markov Modelling (4 sessions)
- Network analysis of a social network, including detection of cliques and central nodes (5 sessions)
Syllabus
- Topic One: Statistical Classification [7 sessions].
Introduction to Sentiment Classification.
Naive Bayes Parameter Estimation.
Statistical Laws of Language.
Smoothing and Statistical Tests.
Overtraining.
Uncertainty and Human Agreement. - Topic Two: Sequence Analysis [4 sessions].
Simple HMM Parameter Estimation.
The Viterbi Algorithm.
Random Baselines and Evaluation Metrics.
Application to Protein Detection Data. - Topic Three: Network Analysis [5 sessions].
Degree, Diameter, Visualisation.
Random Networks and Small World Property.
Betweenness Centrality.
Clique Finding.
Objectives
By the end of the course students should be able to
- understand and program two simple supervised machine learning algorithms;
- use these algorithms in statistically valid experiments, including the design of baselines, evaluation metrics, statistical testing of results, and provision against overtraining;
- visualise and interpret examples of statistical laws of language;
- visualise the connectivity and centrality in large networks;
- use clustering (i.e., a type of unsupervised machine learning) for detection of cliques in unstructured networks.
Recommended reading
Jurafsky, D. & Martin, J. (2008). Speech and language
processing. Prentice Hall.
Durbin, R., Eddy, S., Krough, A. & Mitchison, G. (1998). Biological
sequence analysis: probabilistic models of proteins and nucleic
acids. Cambridge University Press.
Easley, D. and Kleinberg, J. (2010). Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press.