This unit might be offered in 2017, subject to enrolment numbers
This unit will extend the treatment of two or three of the areas of AI introduced in KIT108, adding more depth and having a more technical focus. The specific topics may vary between years, reflecting the expertise of the staff involved in offering the unit in particular years. Students should contact the unit coordinator each year to find out the topics for that year. Possible topics include: Data mining / machine learning (including neural networks), computer vision, genetic algorithms, real-time aspects of AI, knowledge based systems and robotics, AI in Games.
|Unit name||Topics in Artificial Intelligence (Elite)|
|Faculty/School||College of Sciences and Engineering
School of Technology, Environments and Design
|Discipline||Information & Communication Technology|
|Available as student elective?||Yes|
This unit is currently unavailable.
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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.
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* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (see withdrawal dates explained for more information).
Unit census dates currently displaying for 2020 are indicative and subject to change. Finalised census dates for 2020 will be available from the 1st October 2019.
|Band||Field of Education|
Fees for next year will be published in October. The fees above only apply for the year shown.
Please note: international students should refer to this page to get an indicative course cost.
KIT108 and DN/HD in KIT205 or KIT206 or KIT305 or KIT307
You cannot enrol in this unit as well as the following:
Three weeks of intensive classes.
100% in-semester (3 assignments 30%, 30%, 40%)
|Timetable||View the lecture timetable | View the full unit timetable|
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