Study-Unit Description

Study-Unit Description


CODE CCE5107

 
TITLE Pattern Recognition Design and Evaluation

 
LEVEL 05 - Postgraduate Modular Diploma or Degree Course

 
ECTS CREDITS 5

 
DEPARTMENT Communications and Computer Engineering

 
DESCRIPTION The study-unit makes use of ready available libraries, such as Sci-kit Learn (http://scikit-learn.org/) to cover the complete process of engineering prediction functions including data collection, feature engineering and model evaluation.

The study-unit provided basic and in-depth knowledge in concepts such as training, validation and test sets, skewed data sets, overfitting, underfitting, smoothing, training and test error rates, ensemble methods, model selection and assessment, dimensionality reduction, feature selection, cross-validation, bootstrap methods, loss-function, precision and recall curves and confusion matrix.

Study-unit Aims:

The aim of this study-unit is to expose students to a library of machine learning models that can be applied to solve real-life problems. Moreover, the study-unit introduces techniques on how to engineer a pattern recognition system.

Learning Outcomes:

1. Knowledge & Understanding
By the end of the study-unit the student will be able to:

- Identify and discuss concepts in machine learning such as feature engineering, model training and evaluation;
- Describe simple Machine Learning algorithms such as linear regression, logistic regression, Naïve Bayes classifiers, decision trees, clustering and anomaly detection.

2. Skills
By the end of the study-unit the student will be able to:

- Design a machine learning solution targeted for a particular problem;
- Evaluate the performance of such machine learning solutions.

Main Text/s and any supplementary readings:

- The Elements of Statistical Learning. T. Hastie, R. Tibshirani and J. H. Friedman.
- ROC Graphs: Notes and Practical Considerations for Researchers. T. Fawcett.

 
ADDITIONAL NOTES Pre-requisite Qualifications: First Cycle Degree

 
STUDY-UNIT TYPE Lecture and Independent Study

 
METHOD OF ASSESSMENT
Assessment Component/s Assessment Due Resit Availability Weighting
Examination (1 Hour) SEM1 Yes 40%
Assignment SEM1 Yes 60%

 
LECTURER/S Adrian F. Muscat
Gianluca Valentino

 
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the study-unit description above applies to the academic year 2019/0, if study-unit is available during this academic year, and may be subject to change in subsequent years.

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