# 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)