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# COMPM055- Supervised Learning

This database contains 2016-17 versions of the syllabuses. For current versions please see here.

Code COMPM055 (Also taught as: COMPGI01 Supervised Learning) 4 Basic mathematics, Calculus, Probability, Linear algebra 1 Mark Herbster (50%), John Shawe-Taylor (30%), Massi Pontil (20%) This module covers supervised approaches to machine learning. Gain in-depth familiarity with various classical and contemporary supervised learning algorithms, understand the underlying limitations and principles that govern learning algorithms and ways of assessing and improving their performance.

# Content:

The course consists of both foundational topics for supervised learning such as Linear Regression, Nearest Neighbors and Kernelisation as well contemporary research areas such as multi-task learning and optimisation via proximal methods.  In a given year topics will be drawn non-exclusively from the following.

• Nearest Neighbors
• Linear Regression
• Kernels and Regularisation
• Support Vector Machines
• Gaussian Processes
• Decision Trees
• Ensemble Learning
• Sparsity Methods
• Proximal Methods
• Semi-supervised Learning
• Neural Networks
• Matrix Factorization
• Online Learning
• Statistical Learning Theory

# Method of Instruction:

Lecture presentations with associated class problems

# Assessment:

The course has the following assessment components:

•  Written Examination (2.5 hours, 75%)
• Coursework Section (25%)

To pass this course, students must:
• Obtain an overall pass mark of 50% for all sections combined.

For full details of coursework see the course web page.

# Resources:

Text Book 1: The Elements of Statistical Learning: Data Mining, Inference and Prediction, Hastie.T., Tibshirani.R., and Friedman.J., Springer [2001]
Reference Book 1: Pattern Classification, Duda.R.O., Hart.P.E., and Stork.D.G., John Wiley and Sons (2001)
Reference Book 2: Pattern Recognition and Machine Learning, Bishop, Christopher M., Springer (2006)
Reference Book 3: An Introduction to Support Vector Machines, Shawe-Taylor J. and Cristianini N., Cambridge University Press (2000)
Reference Book 4: Kernel Methods for Pattern Analysis, Shawe-Taylor.J, and Cristianini N., Cambridge University Press (2004)