COMP SCI 7317OL - Using Machine Learning Tools PG
Online - Online Teaching 3 - 2024
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General Course Information
Course Details
Course Code COMP SCI 7317OL Course Using Machine Learning Tools PG Coordinating Unit Computer Science Term Online Teaching 3 Level Postgraduate Coursework Location/s Online Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Prerequisites Carousel 2 Courses: COMP SCI 7211OL, DATA 7301OL, DATA 7302OL & MATHS 7027OL Assumed Knowledge Programming experience Restrictions Only available to students in the Master Data Sci. (Applied) (OL). Assessment Assignments and quizzes Course Staff
Course Coordinator: Nordiana Shah
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this you will be able to:
1 Adapt industry-standard software tools to model and solve machine learning tasks on real data sets. 2 Evaluate and identify an appropriate method and tool for a given problem and data set. 3 Discriminate between problems and data sets that are amenable to machine learning methods and those that are not. 4 Analyse results and solutions to verify their correctness and identify sources of error. 5 Assess the significance and validity of solutions obtained by multiple methods. 6 Design data management procedures to enable the 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) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
2, 3, 4, 5 Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
1, 3, 4, 6 Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
6 Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
6 Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
1, 6 Attribute 6: Australian Aboriginal and Torres Strait Islander cultural competency
Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.
. Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
1, 6 Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
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Learning Resources
Required Resources
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, by Aurélien Géron, O'Reilly Media, ISBN: 9781492032649. (Provided by the university library.) -
Learning & Teaching Activities
Learning & Teaching Modes
Teaching and learning modes include weekly online learning content and videos, external resources, practical exercises, group discussions, formative reviews and online tutorials.
Students will be able to communicate with the course coordinator, teacher, tutors and other students in the course online discussion form, during synchronised tutorials, in individual video conferences and by email.
Students are required to be available for synchronous weekly online tutorials.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
The weekly workload is approx. 25 hours and breaks down into activities as follows:
Course Content 6.50 hours External Resources 1.00 hours Practice (Exercises) 5.00 hours Discussion 2.00 hours Formative Review 1.00 hour Tutorial Interaction 1.50 hours Video 0.25 hours Assessment 10.00 hours
Learning Activities Summary
Module 1 Machine Learning Workflow Module 2 Looking Inside Machine Learning Module 3 Deep Neural Networks Module 4 Training Deep Neural Networks Module 5 Convolutional Neural Networks Module 6 Putting it all together -
Assessment
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 Task Task Type Due Weighting Assignment 1 Machine Learning Case Study
(Programming and essay questions)Summative End of Week 2 30% Assignment 2 Deep Neural Networks Training and Evaluation
(Programming and essay questions)Summative End of Week 4 35% Assignment 3 Deep Neural Networks Analysis
(Programming and essay questions)Summative End of Week 6 35% Assessment Detail
Assessment 1: Machine Learning Case Study
In this assessment, you will experiment with a classifier using a standard data set. You will
analyse the data and evaluate the results obtained by the classifier. Based on your
understanding of the method and data, you will measure performance and propose steps
for improvement.
Assessment 2: Deep Neural Networks: Training and Evaluation
In this assessment, you will create a deep neural network model using Keras and
Tensorflow. You will train this model and evaluate its performance against a classical
baseline method.
Assessment 3: Deep Neural Networks Analysis
In this assessment, you will investigate the effects of key parameters on deep neural
network performance. You will base your investigation on study of the data set and an
understanding of neural network optimisation. You will reflect on the results of your
experiments and whether they agree with your predictions.Submission
Assessments are submitted electronically through the assignment feature in MyUni. Turnitin will be used to automatically check for plagiarism. Concise written feedback and grades will be provided via the MyUni feedback feature.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 .
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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.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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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鈥檚 disciplinary procedures.
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