COMP SCI 7317 - Using Machine Learning Tools PG

North Terrace Campus - Semester 1 - 2021

An introduction to the use and application of key machine learning tools. Students will learn to build software that uses pre-existing toolkits as appropriate to solve a variety of machine learning problems. The course will have a practical focus using case studies and worked examples, with an emphasis on ensuring that solutions are valid and verifiable.

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Course Details

Course Code	COMP SCI 7317
Course	Using Machine Learning Tools PG
Coordinating Unit	Computer Science
Term	Semester 1
Level	Postgraduate Coursework
Location/s	North Terrace Campus
Units	3
Contact	Up to 3 hours per week
Available for Study Abroad and Exchange	N
Restrictions	Master of Data Science (Applied) OL Only

Course Staff

Course Coordinator: Dr Anthony Dick

Course Timetable

The full timetable of all activities for this course can be accessed from Course Planner.

Course Learning Outcomes

On successful completion of this course students will be able to:

1. Apply industry standard software tools to model and solve machine learning tasks on real data sets
2. Select an appropriate method and tool for a given problem and data set
3. Distinguish problems and data sets that are amenable to machine learning methods from those that are not
4. Analyse results and solutions to verify their correctness, and identify sources of error
5. Compare the significance and validity of solutions obtained by multiple methods
6. Design data management procedures to enable accurate application of machine learning

University Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

University Graduate Attribute	Course Learning Outcome(s)
Deep discipline knowledge informed and infused by cutting edge research, scaffolded throughout their program of studies acquired from personal interaction with research active educators, from year 1 accredited or validated against national or international standards (for relevant programs)	1, 2, 3, 4, 5
Critical thinking and problem solving steeped in research methods and rigor based on empirical evidence and the scientific approach to knowledge development demonstrated through appropriate and relevant assessment	2, 3, 4
Teamwork and communication skills developed from, with, and via the SGDE honed through assessment and practice throughout the program of studies encouraged and valued in all aspects of learning	5, 6
Career and leadership readiness technology savvy professional and, where relevant, fully accredited forward thinking and well informed tested and validated by work based experiences	2, 3, 6

Learning & Teaching Activities

Learning & Teaching Modes

No information currently available.

Workload

No information currently available.

Learning Activities Summary

No information currently available.

The University's policy on Assessment for Coursework Programs is based on the following four principles:

Assessment must encourage and reinforce learning.
Assessment must enable robust and fair judgements about student performance.
Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
Assessment must maintain academic standards.

Assessment Summary

Assessment will be based solely on assignment work set throughout the course.

Assignments are individual projects focusing on the application of machine learning to real data sets.

Assessment Task	Weighting (%)	Individual/ Group	Formative/ Summative	Due (week)*	Hurdle criteria	Learning outcomes	CBOK Alignment**
Practical assignments	100	Individual	Summative	Weeks 4-12		1, 2. 3. 4. 5. 6.	1.1 1.2 2.2 2.4 2.6 3.1 3.2 4.1 4.3 4.4
Total	100

* The specific due date for each assessment task will be available on MyUni.

This assessment breakdown complies with the University's Assessment for Coursework Programs Policy.

This course has a hurdle requirement. Meeting the specified hurdle criteria is a requirement for passing the course.

**CBOK is the Core Body of Knowledge for ICT Professionals defined by the Australian Computer Society. The alignment in the table above corresponds with the following CBOK Areas:

1. Problem Solving

1.1 Abstraction

1.2 Design

2. Professional Knowledge

2.1 Ethics

2.2 Professional expectations

2.3 Teamwork concepts & issues

2.4 Interpersonal communications

2.5 Societal issues

2.6 Understanding of ICT profession

3. Technology resources

3.1 Hardware & Software

3.2 Data & information

3.3 Networking

4. Technology Building

4.1 Programming

4.2 Human factors

4.3 Systems development

4.4 Systems acquisition

5. ICT Management

5.1 IT governance & organisational

5.2 IT project management

5.3 Service management

5.4 Security management

Assessment Detail

No information currently available.

Submission

No information currently available.

Course Grading

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
Grade	Mark	Description
FNS		Fail No Submission
F	1-49	Fail
P	50-64	Pass
C	65-74	Credit
D	75-84	Distinction
HD	85-100	High Distinction
CN		Continuing
NFE		No Formal Examination
RP		Result Pending

Further details of the grades/results can be obtained from Examinations.

Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

Final results for this course will be made available through Access Adelaide.

Student Feedback

The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
Fraud Awareness

Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator