FIT3154 - Advanced data analysis - 2018

This unit introduces the problem of machine learning and the major kinds of statistical learning used in data analysis. Learning and the different kinds of learning will be covered and their usage discussed. Evaluation techniques and typical application contexts will presented. A series of different models and algorithms will be presented in an exploratory way: looking at typical data, the basic models and algorithms and their use: linear and logistic regression, support vector machines, Bayesian networks, decision trees, random forests, k-means and clustering, neural-networks, deep learning, and others. Finally, two specialist topics will be covered briefly, statistical learning theory and working with big data.

At the completion of this unit, students should be able to:

1. describe what machine learning is;
2. differentiate kinds of statistical learning models and algorithms;
3. evaluate a machine learning algorithm in typical contexts;
4. describe and apply the major models and algorithms for statistical learning;
5. identify the most competitive algorithms for typical contexts;
6. compare and contrast the differences between big data applications and regular applications of algorithms;
7. describe the theoretical limits of learning.

Examination (2 hours): 60%; In-semester assessment: 40%

Minimum total expected workload equals 12 hours per week comprising:

1. Contact hours for on-campus students:
   • Two hours lectures
   • Two hours laboratories
2. Additional requirements (all students):
   • A minimum of 8 hours of personal study per week for completing lab/tutorial activities, assignments, private study time and revision.

See also Unit timetable information

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