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Machine Learning Foundations

A comprehensive introduction to the fundamentals of Machine Learning for IT professionals with no explicit background in the field.

About this course

This microcredential introduces the essential elements of machine learning - a technique that enables a machine to learn from data, to automatically derive, or enhance its strategy to perform tasks.

Taking a research-inspired approach, the microcredential guides participants to apply state-of-the-art algorithms in their professional practice, with a focus on practical applications.

The microcredential presents participants with core concepts in machine learning as well as a generic framework. Basic learning models, including decision trees and linear families demonstrate the theory of machine learning and some real-world applications.

Course Outline

This course has been designed to provide you with an applied introduction to the field of machine learning, and an orientation to its different usages. It has been designed by the UTS Faculty of Engineering and Information Technology, leveraging the Faculty's unique expertise in the area of artificial intelligence.

In this course you will meet (virtually) and work with a dedicated course facilitator, who supports your learning and engagement with the teaching resources designed by the Lead Academic and a team of experts in the Faculty of Engineering and IT at UTS.

There are five modules, each featuring self-study materials and facilitated online sessions.

  • Machine learning problems

This introductory module will provide you with both a theoretical background of machine learning problems as well as ensure you are refreshed on the basic mathematical formulas used in defining machine learning problems.

  • Linear models

In this module we introduce the linear family of hypotheses, which are widely used in machine learning. The basic idea is that the target of interest is linked to a weight sum of all the data attributes. This module introduces the use of linear models, perceptron models, how to train perceptron models, and implementation of linear models.

  • Decision trees

We will look at how a decision tree works to produce the target value from the attributes and cover elements of the information theory that underlies and motivates the development of decision trees. We introduce the ID3 decision tree training algorithm.

  • Evaluation, error and noise

This module covers the criteria employed in the selection and optimisation process.

We cover the loss function for training, the difference between training (in-sample) and generalisation (out-sample) errors, and noise.

  • Ensemble methods

In this module, you are introduced to the idea of putting multiple learned data models together to make a complex and more powerful model. We cover resampling (bootstrapping), bagging and random forest methods, and the adaboost algorithm.


This course is delivered in a scheduled format over ten weeks.

For eight weeks of teaching and learning you will participate each week in an online session where you'll have the chance to apply what you've learned, ask questions and hear from other participants who are taking the course with you. The workshops are led by the course facilitator.

Weeks nine and ten are focused on giving you time to complete the final assignment, with support and scheduled Q&A sessions provided.

Key benefits of this micro-credential

A combination of in-depth theoretical and practical study across the whole lifecycle of a data model.

Dive deep into the theoretical design motivation and dynamics of the machine learning model, and translate mathematical notions into data structures and programs to get some hands-on experience of how the models work.

Complete as a self-contained, stand-alone program, or combine with other microcredentials to stack towards potential future degree coursework study with UTS.

Who is this course for? 

This microcredential is accessible to participants with basic mathematical proficiency (linear algebra and statistics) and some programming experience (not specifically in Python).

Course Information

Course learning objectives

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

You will be able explain core concepts in machine learning, along with some practical application contexts, to a peer.

Teaching and learning strategies

This microcredential includes weekly, live, one-hour online workshops facilitated by an expert UTS academic, supporting self-study and online learning activities. Case studies of real-world business illustrate applications of data mining techniques. The workshop sessions focus on hands-on experience in data mining and data analytics tools, and the understanding and interpretation of the results. Regular formative quizzes throughout the microcredential will allow participants to gauge their progress.

Assessment criteria

The graded assessment task is an individual task to implement a machine learning algorithm. Build a data model, adapt the model to observed data, and test against data evaluation criteria. A written report describing the implementation is evaluation is submitted.

Length: 2,000 words

Minimum requirements

In order to pass the microcredential, participants must achieve an overall mark of 50% or more.

Required texts


Participant requirements and equipment

To complete this online course you will need a personal computer with reliable internet access and the ability to run the latest version of our supported Internet browsers, Zoom for online meetings and classes, and Python.



28 September




8 wks


5 hrs p/w

Book a session

Mon 28 Sep 2020 -
Fri 04 Dec 2020
  • 28 September - 4 December 2020, 9am-5pm
  • Online

Tutorials will be scheduled for one-hour sessions every Wednesday afternoon, and live Q&A sessions every Friday afternoon.

Final dates and times for live sessions will be confirmed to registered learners prior to the course starting. Note that recordings of all live sessions will be made available for reference.

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