Skip to main content
Close icon Menu icon
UTS UTS
Cart
Your cart
Your cart is empty

Browse courses to find something that interests you.

Log in
Search
Enter to search / ESC to close


Browse Study Areas >

Analytics & Data Science

  • Critical & Creative Thinking
  • Data Science
  • Entrepreneurship
  • Invention & Innovation
  • Leadership & Management
  • Problem Solving
  • Strategic Thinking

Architecture & Built Environment

Business & Transformation

  • Accounting & Finance
  • Business Technology
  • Critical & Creative Thinking
  • Data Science
  • Economics
  • Entrepreneurship
  • Leadership & Management
  • Marketing
  • Problem Solving
  • Strategic Thinking

Communication & Media

Design Innovation

Technology

  • Business Technology
  • AI & Machine Learning
  • Telecoms
  • Data Science
  • Key Technology Trends

Health

Law

  • Law and technology
  • Substantive law
  • Industry accreditation
  • Professional skills

Public Policy & Governance

  • Evidence Evaluation and Insights
  • Community engagement
  • Governance
  • Local government
  • Leadership
  • Policy and program delivery
  • Strategic planning

Systems & Operations

Engineering

Bridging Courses

Science & Mathematics

Education

Search
Log in
  • About
  • All courses

    All courses
    Browse all courses
    • Analytics & Data Science
    • Architecture & Built Environment
    • Business & Transformation
    • Communication & Media
    • Design Innovation
    • Technology
    • Health
    • Law
    • Public Policy & Governance
    • Systems & Operations
    • Engineering
    • Bridging Courses
    • Science & Mathematics
    • Education
  • For your organisation
  • Insights
  • australian retirement trust
  • Home
  • Study areas
  • Engineering
  • Advanced Machine Learning
MICROCREDENTIAL

Advanced Machine Learning

Enrol now
$1,595.00

START DATE

Enquire now

MODE

Other in-person location

DURATION

10 wks

COMMITMENT

10 wks avg 5 hrs/wk

Join Waitlist








Have a question?
Make an enquiry

Designed for professionals with some familiarity with machine learning (ML), this microcredential will provide a deeper understanding of statistical learning theory and empirical risk minimisation, allowing participants to improve their ML models and algorithms. The microcredential covers both theoretical considerations and hands-on, under-the-hood coding exercises.

About this microcredential

This microcredential introduces some advanced machine learning data models, algorithms and theoretical results. It focuses on key considerations, such as building data models with neural networks, deep neural network (DNN) architecture and generalised linear models and kernel methods and learning data models covering gradient-based algorithms and optimisation, backpropagation and constrained optimisation practice.

It also considers improving model reliability using DNN structures to enable learning stability and regularisation techniques, as well as exploring why learned models can be trusted through the risk theory of learning-based models, looking at bias, variance, training and test evaluation.

Key benefits of this microcredential

  • Upgrade your machine learning models and projects – develop the knowledge and skills to build and understand more reliable models
  • Gain an in-depth coverage of the theoretical models and considerations underpinning machine learning and some practical coding exercises to demonstrate them
  • Complete as a self-contained course, or as a potential pathway to future postgraduate study.

This microcredential aligns with the 2 credit point subject, Advanced Machine Learning (42894) in the Graduate Certificate in Professional Practice (C11298), Graduate Diploma of Professional Practice (C06136), Master of Professional Practice (C04404), Graduate Certificate in Technology (C11301), Graduate Diploma in Technology (C06137) and Master of Technology (C04406).

This microcredential may qualify for recognition of prior learning at this and other institutions.

Who should do this microcredential?

This microcredential is suitable for professionals from a wide range of sectors and backgrounds, who have completed the Machine Learning Foundations microcredential, or otherwise have some professional experience in the field and are comfortable working in Python.

UTS microcredentials are developed for professionals with a capacity to undertake postgraduate tertiary education.

Price

Full price: $1,595.00 (GST-free)*

*Price subject to change. Please check price at time of purchase. 

Enrolment conditions

  • Course purchase is subject to UTS Open Terms and Conditions. 

COVID-19 response 

  • UTS complies with latest Government health advice. Delivery of all courses complies with the UTS response to COVID-19.

Additional course information

Course outline

During this course you will meet and work with a dedicated course facilitator who will support your learning and engagement with teaching resources designed by the lead academic and team of experts from the Faculty of Engineering and IT.

The course is structured into five modules. Each module comprises self-study materials and facilitated online sessions. The five modules and key topics covered are:

Module 1 - Neural networks

  • This module covers the construction, computation and training of neural networks. You will develop hands-on experience of building neural network models and knowledge of the state-of-the-art NN models in different application areas.

Module 2 - Machine learning theory

  • This module covers a discussion of learning from experience and goes into depth on Hoeffding’s inequality to consider the bounds on reliability.

Module 3 - Convolutional neural networks

  • This module covers the motivation behind convolutional neural networks and focuses on the computation and backpropagation of a convolutional layer.

Module 4 - Transformer families

  • This module introduces how self-correlation can be useful in a learning model and how to represent the correlation and implement the model as a block of neural networks. Participants will get hands-on experience in building a family of neural network from scratch and applying a transformer model to a practical data set.

Module 5 - Generative models

  • This module introduces generative adversarial networks (GANs), featuring a definition of the GAN model, the core training steps of GANs and a detailed walkthrough of implementing a GAN and evaluating the results.

Course delivery

This course includes weekly, live, one-hour online tutorials and one-hour weekly Q&A sessions facilitated by an expert UTS academic, supporting self-study and online learning activities.

Both theoretical and practical learning are covered. The course provides a systematic view of the whole life-cycle of a data model, from the design motivation to the dynamics in the learning process and the evaluation and how reliable the evaluation results are. On the practical side, participants will translate mathematical notions into data structures and programs in a digital computer, which allows them to ‘open the hood’ of the data models and get hands-on experience to examine piece-by-piece how the models perform learning.

Regular formative quizzes throughout the microcredential will allow learners to gauge their progress.     

Course learning objectives

Upon successful completion of this course you will be able to design machine learning algorithms with practical implementation for professional contexts.        

Assessment

The graded assessment task comprises an individually written assessment on implementing one of the advanced machine learning algorithms, i.e., building a data model and adapting the model to observed data - according to the machine learning framework. The model will be tested against data and assessed using the evaluation criteria introduced in the course. The implementation process and evaluation are to be summarised in a report.

Length: 2,000 words      

Requirements

Mandatory

  • To complete this online course, you will need a personal computer with reliable internet access, web conferencing capability and an operating system with a web browser compatible with the Canvas LMS. You will need to be able to run a currently-supported version of Python.

Related Courses

Dynamic Managerial Capability Dynamic Managerial Capability
Microcredential

Dynamic Managerial Capability

Develop dynamic management techniques that will transform the way you do business. [7 wks, avg 10 hrs/wk]

  • calendar_month Enquire now
  • hourglass_empty 7 wks
  • import_contacts 7 wks avg 10 hrs/wk
  • monetization_on 3,001
  • place On Campus (Sydney)
  • supervisor_account Scheduled sessions
Structured Thinking and Planning Structured Thinking and Planning
Microcredential

Structured Thinking and Planning

Learn the complex problem-solving approaches used by the world’s top management consultancies [7wks, avg 10 hrs/wk]

  • calendar_month Enquire now
  • hourglass_empty 7 wks
  • import_contacts Avg 10 hrs p/w
  • monetization_on 2,988
  • place Online
  • supervisor_account Scheduled sessions
Practising Inclusion: Working and Teaching for Social Justice Practising Inclusion: Working and Teaching for Social Justice
Microcredential

Practising Inclusion: Working and Teaching for Social Justice

Learn to build inclusive practices in higher education environments by deepening your understanding of diversity, equity, social justice and inclusion. [6 wks Avg, 12hrs/wk]

  • calendar_month Enquire now
  • hourglass_empty 6 wks
  • import_contacts 6 wks avg 12 hrs/wk
  • monetization_on 1,286
Infrastructure Law: Delivering projects and managing risks Infrastructure Law: Delivering projects and managing risks
Microcredential

Infrastructure Law: Delivering projects and managing risks

Explore the legal and commercial elements of major infrastructure projects and learn to allocate risk effectively. [6 wks, avg 10 hrs/wk]

  • calendar_month Enquire now
  • hourglass_empty 6 wks
  • import_contacts Avg 10 hrs/wk
  • monetization_on 2,500
  • place Online
  • supervisor_account Scheduled sessions
Ethical AI for Good Business Ethical AI for Good Business
Microcredential

Ethical AI for Good Business

Set the ethical technology agenda for your organisation grounded in AI literacy, knowledge and agency. [6 wks, avg 11 hrs/wk]

  • calendar_month Enquire now
  • hourglass_empty 6 wks
  • import_contacts 6 wks avg 11 hrs/wk
  • monetization_on 2,397
Expert Design Secrets To Radically Up Your DataVis Game Expert Design Secrets To Radically Up Your DataVis Game
Microcredential

Expert Design Secrets To Radically Up Your DataVis Game

Learn powerful design principles for making better, more persuasive charts and infographics. [6 wks, avg 10 hrs/wk]

  • calendar_month Enquire now
  • hourglass_empty 6 wks
  • import_contacts 6 wks avg 10 hrs/wk
  • monetization_on 2,195
We use cookies

We use cookies to help personalise content, tailor and measure ads, plus provide a safer experience. By navigating the site, you agree to the use of cookies to collect information. Read our Cookie Policy to learn more.

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.

  • About
    • About UTS Open
    • The University
    • FAQ
  • UTS Degrees
    • Postgraduate study
    • Find a course
  • News
    • Insights
    • Events
    • Find an expert
  • Information
    • Terms and Conditions
    • UTS governance
    • Privacy
    • Accessibility
    • Disclaimer
Connect
  • Contact us
  • Facebook Twitter Instagram Youtube LinkedIn

© Copyright UTS - CRICOS Provider No: 00099F - TEQSA Provider ID: PRV12060 - TEQSA Category: Australian University - ABN: 77 257 686 961 - 09 June 2022 03:33 PM. The page is authorised by the Chief Operating Officer and Vice-President (COO)

loading