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A study of computational processes that underlie learning in machines. The course covers fundamental theories and algorithms in machine learning.
2022 Covid-19 Update: Please refer to the course page on AKO | Learn for all information about your course, including lectures, labs, tutorials and assessments.In order to complete the course successfully, students should be familiar with:- basic linear algebra (operations on vectors and matrices, inverse matrices, eigen-values/vectors, vector geometry, etc); - basic calculus (limits, differentiation and integration and their applications, partial derivatives, chain rule, etc);- basic probability (marginal and conditional probabilities, discrete and continuous random variables, expectation, etc); and- basic logic and set theory.Machine Learning is the study of how computer programs can improve their performance automatically through experience, or "learn from experience". At the end of this course you will have a broad overview of this rapidly growing area. You will also be able to critically assess the potential of various Machine Learning paradigms and techniques.The course covers major paradigms of learning including supervised, unsupervised, and reinforcement learning. Some of the topics discussed include:- Learning theory- Decision trees- Linear models- Kernels and Support Vector Machines- Probabilistic models (parametric and non-parametric methods)- Graphical models, neural networks, and deep learning- Reinforcement learning- Ensemble learning- Unsupervised learning
After successful completion of this course, students will be able todemonstrate their understanding of a wide variety of learning algorithms;objectively compare the performance of any machine learning algorithm based on various metrics;apply a variety of learning algorithms to data;demonstrate deep understading of some of machine learning algorithms and implement a working version of them;demonstrate a good understanding of the fundamental issues and challenges of machine learning: data, representation, feature and model selection, model complexity, under- or over-fitting, etc; andunderstand the underlying theoretical and mathematical foundations of the field.
(i) COSC367; (ii) At least 45 points of 100-, 200- or 300-level MATH/EMTH/STAT (but not including MATH101, MATH110, STAT101); (iii) Subject to approval by the Head of Department
Students must attend one activity from each section.
Please note that the course activity times advertised here are currently in draft form, to be finalised on Monday 30 January 2023 for S1 and whole year courses, and Monday 26 June 2023 for S2 courses. Please hold off enquiries about these times until those finalisation dates.
Covid-19 Update: Please refer to the course page on AKO | Learn for all information about your course, including lectures, labs, tutorials and assessments.
Bishop, Christopher M;
Pattern recognition and machine learning
Mitchell, Tom M.1951-;
Shalev-Shwartz, Shai. , Ben-David, Shai;
Understanding machine learning :from theory to algorithms
Cambridge University Press, 2014.
Witten, I. H. , Frank, Eibe., Hall, Mark A;
Data mining : practical machine learning tools and techniques
Morgan Kaufmann, 2011.
The Computer Science department's grading policy states that in order to pass a course you must meet two requirements:1. You must achieve an average grade of at least 50% over all assessment items.2. You must achieve an average mark of at least 45% on invigilated assessment items.If you satisfy both these criteria, your grade will be determined by the following University-wide scale for converting marks to grades: an average mark of 50% is sufficient for a C- grade, an average mark of 55% earns a C grade, 60% earns a C+ grade and so forth. However if you do not satisfy both the passing criteria you will be given either a D or E grade depending on marks. Marks are sometimes scaled to achieve consistency between courses from year to year.Students may apply for special consideration if their performance in an assessment is affected by extenuating circumstances beyond their control.Applications for special consideration should be submitted via the Examinations Office website within five days of the assessment. Where an extension may be granted for an assessment, this will be decided by direct application to the Department and an application to the Examinations Office may not be required. Special consideration is not available for items worth less than 10% of the course.Students prevented by extenuating circumstances from completing the course after the final date for withdrawing, may apply for special consideration for late discontinuation of the course. Applications must be submitted to the Examinations Office within five days of the end of the main examination period for the semester.
Domestic fee $1,079.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Computer Science and Software Engineering