COSC401-19S2 (C) Semester Two 2019

Machine Learning

15 points
15 Jul 2019 - 10 Nov 2019

Description

A study of computational processes that underlie learning in machines. Topics will include inductive, analytical and case-based learning, support vector machines and graphical models.

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
- Feature selection and construction
- Ensemble learning
- Unsupervised Learning

Learning Outcomes

  • After successful completion of this course, students will be able to

  • demonstrate 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; and
  • understand the underlying theoretical and mathematical foundations of the field.

Pre-requisites

(i) COSC367; (ii) At least 45 points of 100-, 200- or 300-level MATH / STAT (but not including MATH101, STAT101); (iii) Subject to approval of the Head of Department

Timetable 2019

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 11:00 - 13:00 Jack Erskine 443 15 Jul - 25 Aug
9 Sep - 20 Oct

Course Coordinator

Kourosh Neshatian

Assessment

Assessment Due Date Percentage 
Assignment One 22.5%
Assignment 2 22.5%
Final Exam 55%

Textbooks / Resources

Recommended Reading

Bishop, Christopher M; Pattern recognition and machine learning; Springer, 2006.

Mitchell, Tom M.1951-; Machine Learning; McGraw-Hill, 1997.

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; 3rd ed; Morgan Kaufmann, 2011.

Additional Course Outline Information

Grade moderation

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 B- 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.

Indicative Fees

Domestic fee $1,002.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Computer Science and Software Engineering.

All COSC401 Occurrences

  • COSC401-19S2 (C) Semester Two 2019