By the end of the module students should be able to:

• Construct a basic text-based search engine, including: Text normalization; Implementation of a Document Index; Calculation of Term‐Frequency and Inverse Document Frequency similarity between queries and documents.
• Understand the basic principle of Latent Semantic Analysis.
• Implement maximum likelihood estimation of Gaussian PDF and Gaussian Mixture PDF parameters for a given data set.
• Understand the basic principle of Principle Components Analysis.
• Understand and apply agglomerative, divisive and k-means clustering algorithms.
• Understand the basic principles of Neural networks.
• Demonstrate an in-depth understanding of hidden Markov models (HMMs) for modelling time-varying data.
• Demonstrate an understanding of employment of HMMs for automatic speech recognition.
• Explain the basic principles of human speech production and perception and use the language of elementary phonetics.
• Understand basic spectral and spectro-temporal analysis of time-varying data.
• Develop an HMM-based speech recognition system using available software tools.