units

FIT5201

Faculty of Information Technology

Postgraduate - Unit

This unit entry is for students who completed this unit in 2015 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

print version

6 points, SCA Band 2, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

LevelPostgraduate
FacultyFaculty of Information Technology
OfferedNot offered in 2015

Notes

Monash Online offerings are only available to students enrolled in the Graduate Diploma in Data ScienceGraduate Diploma in Data Science (http://online.monash.edu/course/graduate-diploma-data-science/?Access_Code=MON-GDDS-SEO2&utm_source=seo2&utm_medium=referral&utm_campaign=MON-GDDS-SEO2) via Monash Online.

Synopsis

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.

Outcomes

On successful 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.

Assessment

In-semester assessment: 100%

Workload requirements

Minimum total expected workload equals 144 hours per semester comprising:

(a.) Contact hours for on-campus students:

  • Two hours/week lectures
  • Two hours/week laboratories

(b.) Contact hours for Monash Online students:

  • Two hours/week online group sessions
  • Online students generally do not attend lecture, tutorial and laboratory sessions, however should plan to spend equivalent time working through resources and participating in discussions.

(c.) Additional requirements (all students):

  • A minimum of 8 hours per week of personal study (22 hours per week for Monash Online students) for completing lab/tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

See also Unit timetable information

Prerequisites