ENGG6300
10 units
6000 level
Course handbook
Description
Through this course students will learn and apply a range of machine learning tools and techniques to typical engineering focused problems. This course contains a strong laboratory element for students to apply the tools using Python as the implementation means, and draws strongly from foundational knowledge of Physics, Calculus and Linear Algebra. A major theme of this courses is to introduce flexible model structures and incorporate fundamental limits (such as standard laws of conservation) ensuring that model predictions obey physical constraints. The course will cover a range of methods used in statistical machine learning and will review relevant areas of statistics and probability theory as required. These methods will be studied and applied to real engineering data from various applications throughout the course. The course also covers important practical considerations such as cross-validation, model selection and the bias-variance trade-off. The course includes theory (e.g. derivations and proofs) as well as practice (labs and assignments) and is delivered using a problem-based format.
Availability2024 Course Timetables
Callaghan
- Semester 2 - 2024
Learning outcomes
On successful completion of the course students will be able to:
1. Implement solutions to different problem domains using Python and propose appropriate flexible models for a range of problems.
2. Quantify the uncertainty of predictions and estimates.
3. Select a model using cross-validation and bias-variance trade-offs.
4. Design and train deep neural-networks for classification.
5. Construct Gaussian Process models for nonparametric modelling.
6. Apply machine learning methods to large-data problems.
7. Design solutions that adhere to fundamental limits, where appropriate.
Content
Topics to be covered include:
- Review of relevant topics in statistics and probability
- Linear regression (including ridge regression and the Lasso)
- Classification via logistic regression and k-nearest neighbours
- Linear and quadratic discriminant analysis
- Regression & classification trees (including bagging and random forests)
- Boosting
- Neural networks and deep learning
- Bayesian non-parametric methods (including the Gaussian process)
- Incorporating fundamental limits into solutions
- Introduction to Python
Assumed knowledge
MATH1110 Mathematics for Engineering, Science and Technology 1, MATH1120 Mathematics for Engineering, Science and Technology 2, MATH2310 Calculus of Science and Engineering, ENGG1003 Introduction to Procedural Programming.
Assessment items
Tutorial / Laboratory Exercises: Lab Exercises
Written Assignment: Assignments
Contact hours
Semester 2 - 2024 - Callaghan
Laboratory-1
- Face to Face On Campus 4 hour(s) per week(s) for 13 week(s) starting in week 1
Lectorial-1
- Face to Face On Campus 2 hour(s) per week(s) for 13 week(s) starting in week 1
Course outline
Course outline not yet available.
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