COMPM090 - Applied Machine Learning
This database contains 2016-17 versions of the syllabuses. For current versions please see here.
|Code||COMPM090 (also taught as COMPGI09)|
|Taught By||David Barber (100%)|
Applied Machine Learning aims to cover some of the issues that may arise in the practical application of machine learning in real-world problems. In addition, the course will cover some of the mathematics and techniques behind basic data analysis methods for both static and time-series data.
|Learning Outcomes||The ability to: assess the effectiveness of solutions presented and to question them in an intelligent way; synthesise solutions to general open-ended problems covering material from the whole programme, tempered with information on commercial reality obtained from this course.|
Multivariate optimisation methods including line search, conjugate gradients and Newton's method, stochastic gradient descent, distributed optimisation.
Neural Nets and deep learning, fast nearest neighbour methods, large scale linear learning.
PCA, Canonical Correlation Analysis, matrix factorisation methods.
Gaussian Mixture Models Gaussian Process Regression/Classification
HMMs, AR models.
Method of Instruction:
Lecture presentations with associated class problems.
The course has the following assessment components:
- Written Examination (2.5 hours, 50%)
- Coursework Section. The coursework is based on assessed practical challenges hosted by Kaggle (50%).
To pass this course, students must:
- Obtain an overall pass mark of 50% for all sections combined
- Obtain a minimum mark of 50% in each component.
To be notified as the course progresses, according to the business themes covered.