Pattern recognition, machine learning and deep learning are closely related topics in the field of artificial intelligence (AI) and its applications in robotics, computer vision, natural language processing, data science, and many others. This unit is an advanced unit in artificial intelligence that focuses on the core element of modelling and recognising patterns in data through learning. Application areas are in analysing data from images and video, healthcare, finance, sports, text documents, speech, human-machine interaction and many more. This unit covers selected topics from Bayesian Inference, Deep Neural Networks, Support Vector Machines/Regression, Graphical Models, and Mixture Models as well as ethical and privacy considerations around the use of AI. Students will gain both an understanding of the theoretical foundations as well as hands-on experience in implementing and using machine learning techniques in real-world applications.
This unit may be cotaught with 11512 Pattern Recognition and Machine Learning PG.

Learning outcomes

After successful completion of this unit, students will be able to:
1. Understand, describe and critique pattern recognition, machine learning and deep learning techniques;
2. Identify and select suitable modelling, learning and prediction techniques to solve a problem;
3. Design and implement a machine learning solution; and
4. Appraise ethical and privacy issues of artificial intelligence techniques.

Graduate attributes

1. ºÚÁϳԹÏÍø graduates are professional - communicate effectively
1. ºÚÁϳԹÏÍø graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. ºÚÁϳԹÏÍø graduates are professional - employ up-to-date and relevant knowledge and skills
1. ºÚÁϳԹÏÍø graduates are professional - take pride in their professional and personal integrity
1. ºÚÁϳԹÏÍø graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
1. ºÚÁϳԹÏÍø graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
2. ºÚÁϳԹÏÍø graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
2. ºÚÁϳԹÏÍø graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
2. ºÚÁϳԹÏÍø graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. ºÚÁϳԹÏÍø graduates are global citizens - communicate effectively in diverse cultural and social settings
2. ºÚÁϳԹÏÍø graduates are global citizens - make creative use of technology in their learning and professional lives
2. ºÚÁϳԹÏÍø graduates are global citizens - think globally about issues in their profession
2. ºÚÁϳԹÏÍø graduates are global citizens - understand issues in their profession from the perspective of other cultures
3. ºÚÁϳԹÏÍø graduates are lifelong learners - adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas
3. ºÚÁϳԹÏÍø graduates are lifelong learners - be self-aware
3. ºÚÁϳԹÏÍø graduates are lifelong learners - evaluate and adopt new technology

Prerequisites

11372 Introduction to Data Science OR
Both 4478 Introduction to Information Technology and 4483 Software Technology 1

Corequisites

None.

Incompatible units

11512 Pattern Recognition and Machine Learning PG

Equivalent units

None.

Assumed knowledge

None.