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COMPGI01 - Supervised Learning

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

Code COMPGI01 (Also taught as: COMPM055 Supervised Learning)
Year MSc
Prerequisites Basic mathematics, Calculus, Probability, Linear algebra
Term 1
Taught By Mark Herbster (50%), John Shawe-Taylor (30%), Massi Pontil (20%)
Aims This module covers supervised approaches to machine learning.
Learning Outcomes 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 
  • Multi-task Learning
  • 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)