COMP8535
Engineering Data Analytics
- Credit
- 6 units
- Catalogue year
- 2027
- Recorded offerings
- First Semester
Course information
Data-driven decision-making is an essential component of emerging systems that generate and consume very large amounts of sensing data from autonomous vehicles to digital pathology. This course covers technologies and methodologies necessary for inferring useful information and identifying underlying patterns from often raw, incomplete, noisy and corrupted data that is present in real-life applications. It will also give students the opportunity to explore advanced solutions of data analytics such as dimensionality reduction, sparse encoding, compressive sensing, nonlinear filtering, manifold learning, and generative data modelling.
Requisite and Incompatibility
To enrol in this course you must be studying: Master of Computing (Advanced) OR Master of Machine Learning and Computer Vision AND have completed 12 units of 6000-level COMP coded courses.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Describe a number of models for inference from data
Assess the strength and weakness of each of these models
Interpret the mathematical equations from Linear Algebra, Statistics, and Probability Theory used in the learning models
Implement efficient data analysis algorithms on a computer
Design test procedures in order to evaluate a model
Combine several models in order to gain better results
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