COMP SCI 3317 - Using Machine Learning Tools
North Terrace Campus - Semester 1 - 2023
General Course Information
Course Code COMP SCI 3317 Course Using Machine Learning Tools Coordinating Unit Computer Science Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Course Description 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.
Course Coordinator: Dr Feras DayoubCourse Tutors:
- Dr Feras Dayoub
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn 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)
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.
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.
2, 3, 4
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.
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.
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, 3, 5
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.
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.
Required ResourcesAll essential resources for this course will be provided online via the MyUni platform.
Recommended ResourcesA recommended textbook is available electronically through the library - details available on the MyUni course page.
Learning & Teaching Activities
Learning & Teaching ModesThe course will be delivered through the following activities:
- Prerecorded lectures
- Weekly recommended chapters from the textbook
- Practice quizzes and graded quizzes focused on the core topics from the lectures and the textbook
Prerecorded lectures will introduce and motivate the key concepts of each topic and discuss details that will help students prepare for the assigned textbook chapters, quizzes, and assignments. Practical assignments and practice quizzes will build on the material covered in the lectures, textbook, and workshops, working with real datasets and reinforcing the practical application of concepts and details.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Students are expected to spend approximately 12 hours per week on this course.
This would typically consist of 3 hours of contact time for lectures plus workshops per week, as well as 2-3 hours of additional independent study and completion to workshops, and an average of 6-7 hours per week on assignment work.
Learning Activities SummaryMyUni hosts the learning activities for the course, including both practice and graded quizzes, recorded lectures and workshop material.
Learning activities, both online and face-to-face, are build on each other throughout the course. Through this learning experience, you will be asked to draw on a range of lower-order and higher-order thinking skills, especially programming and applied mathematics.
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 SummaryAssessment will be based mainly on three assignments, spaced throughout the course, but also includes a small proportion for two assessed multiple choice quizzes and preliminary workshop exercises.
Assignments are individual projects focusing on the application of machine learning to real data sets.
Assessment Task Weighting (%) Individual/Group Fomative/
Due (week)* Hurdle criteria Learning outcomes CBOK Alignment** Practical assignments and Quizzes (3) 80 Individual Summative Weeks 5-12 1-6 1.1 1.2 2.2 2.4 2.6 3.1 3.2 4.1 4.3 4.4 Multiple Choice Quizzes (2) 20 Individual Summative Weeks
2-6 1.1 2.1 2.2 2.5 2.6 3.1 3.2 4.2 Preliminary workshop exercises (6) 0 Individual Formative Weeks 1-4 & 6,7 & 9-11 1-6 1.1 1.2 2.2 2.4 2.6 3.1 3.2 4.1 4.3 4.4 Total: 100
This assessment breakdown complies with the University's Assessment for Coursework Programs Policy.
**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
2. Professional Knowledge
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
4. Technology Building
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 Related RequirementsWorkshop sessions involving assessed preliminary exercises should be attended in order to submit the answers, unless special prior alternative arrangements are made.
Assessment DetailPractical assignments and quizzes (Weighting 80%): Students are required to extend workshop material to write programs that perform a machine learning task on real data uses best practice methods. This requires application of both lecture and workshop material as well as small additional pieces of syntax or functionality to be determined independently.
Online multiple-choice quizzes (Weighting 20%): These quizzes test students' understanding of key concepts, theories, and methodologies related to machine learning. The quiz questions cover a wide range of topics, including but not limited to: data preprocessing, regression analysis, classification, clustering, deep learning, neural networks, and evaluation metrics.
SubmissionAll assessment solutions are to be submitted through MyUni.
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.
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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