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This course examines suitable nutrition to animals in order to obtain good animal health management. It covers estimating the nutritive value of feeds; estimating the nutrient requirements of animals and diet formulation. The focus is on building up knowledge on animal nutrition by assessments of nutritional adequacy and solving of nutritional problems, with a particular emphasis on wildlife and animals used in agricultural production systems.

This course examines introductory level scientific and engineering design concepts including sustainable development, and product and processing line management with an emphasis on sustainable manufacturing as the core theme throughout the course. A technical lecture series will demonstrate the integration of all aspects of food science and technology, and their underpinning by the basic sciences, through examination of a hypothetical company producing a selected food product.  

This course examines major topics in pattern recognition, particularly aspects of classification and decision. Students will gain effective pattern recognition tools with which to analyze the often vast amounts of diverse data in research applications. 

Topics include introduction to pattern recognition - machine perception - PR systems and design cycle, Bayesian decision theory for continuous features - Bayes Decision Rule - minimum-error-rate classification - classifiers, normal density, discriminant functions and discrete Bayesian decision theory - discriminant functions for the normal density - error probabilities and integrals - Bayes Decision Theory for discrete features, maximum-likelihood and Bayesian parameter estimation - Bayesian parameter estimation: Gaussian case - Bayesian parameter estimation: general theory - HMM, nonparametric techniques - density estimation - Parzen windows - nearest neighbor estimation (NN, k-NN) - fuzzy classification, linear discriminant functions i - linear discriminant functions and decision surfaces - generalized linear discriminant functions - minimizing the perceptron criterion function, relaxation procedures, linear discriminant functions ii - minimum square-error procedures - relation to Fishers linear discriminant - the Widrow-Hoff and Ho-Kashyap procedures - multicategory generalizations - ridge regression and its dual form [2] - classification error based method [2], model assessment and performance evaluation - bias, variance and model complexity [2] - model assessment and selection 2] - confusion matrix, error rates, and ROC [2] - statistical inference [2] - statistical errors [2], dimension reduction and feature extraction - principal component analysis - Fisher linear discriminant - nonlinear projections, support vector machines - introduction - SVM for pattern recognition [2] - linear support vector machines [2] - nonlinear support vector machines [2], multilayer neural networks - introduction - feedforward operation and classification - backpropagation algorithm - some issues in training neural networks - key ideas in classification, introduction to deep learning networks - convolutional neural networks (CNN) - autoencoders - deep belief networks - deep reinforcement learning - generative adversarial networks (GAN), algorithm-independent machine learning - introduction - bias and variance - resampling for classifier design - estimating and comparing classifiers - combining classifiers, unsupervised learning and clustering - mixture densities and identifiability - maximum-likelihood estimates - application to normal mixtures. 

Prerequisites: Linear Algebra, Probability, MATLAB, Python, or C-Programming Skills 

Mathematics is at the same time a conceptual framework, a collection of proven theorems, and a toolbox. In this course, students encounter all three of these aspects by studying one of the central mathematical issues for applications in science and engineering. The general topic of the course is the solution of linear partial differential equations using the separation of variables, Fourier series, and Fourier transforms. The study involves both computational and rigorous mathematical aspects. While the actual computation of solutions is the main objective, students also learn the mathematical theorems establishing the validity and limitation of the different methods. Interested students are also offered the possibility to experiment with numerical approaches.  In addition to the contact hours, each student is expected to work nine hours a week on the course. This time should be devoted to reviewing the material of the preceding lecture; finishing the exercises started in the preceding problem session; preparing exercises to hand in; studying the corrections of the previously returned hand-in problems and making sure everything is clear. Entry Requirements: Calculus and Linear Algebra.

This course covers basic knowledge of modern biology that students studying natural sciences must have with an emphasis on life phenomena from a molecular interpretation. 

Topics include hormones, sensory organs, integration and coordination of the nervous system, movement, classification of organisms, ecology, and behavior. 

The 2020s have seen the rise of numerous strategic problems for Australia. There are giant states in fierce competition, such as the United States and China, and emerging giants in India and Indonesia. There are also problems from below, such as climate change, artificial intelligence, cyber security, and terrorism. This course examines the security challenges facing Australia and explores how Australia should approach its region. 

This course focuses on applications of basic statistical techniques. In particular, we explore model formulation, model fitting, interpretation and presentation of analysis results for simple and multiple linear regressions, and logistic regression models. Some applications to data from the field of Agriculture, Biology , Economics, Finance etc. will be explain various concepts. Applications using R statistical software is also considered.

This course delves into the multifaceted landscape of social policies across Europe, examining the diverse approaches taken by various nations to address social welfare challenges. The course begins by providing a foundational understanding of social policy concepts and theories. It then transitions into an in-depth analysis of the evolution of welfare systems in Europe from the post-war period to the present day. Emphasis is placed on understanding the historical legacies, institutional frameworks, and ideological underpinnings that influence the design and restructuring of social policies in different European countries. The course finally delves into the comparative examination of key areas of social policy, including family policy, labor market policy, healthcare, and long-term care.

This course examines the body of law known as International Law or sometimes ‘Public International Law', as distinct from ‘Private International Law'. The field of International Law deals with many aspects of the functioning of the international community (including the relations of States with each other and with international organizations); it also affects many activities that occur within or across State boundaries (including the treatment by States of their citizens, environmental law, military operations, and many other areas). 

This course explores changes to our global environment in the Anthropocene and practices used to solve these impacts. It poses questions of current sustainability and global system failure, such as can we design a society and economy that is sustainable, democratic, and prosperous? This course uses a broad interdisciplinary approach to build understanding of central issues of sustainability. Students critically examine sustainability through the lens of culture and societal change, political conflict, ecological economics, global environmental issues, globalization and development and ecological design.