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Showing 25 course outlines from 3701 matches
2876
STATS 763
: Advanced Regression Methodology2021 Semester One (1213)
Generalised linear models, generalised additive models, survival analysis. Smoothing and semiparametric regression. Marginal and conditional models for correlated data. Model selection for prediction and for control of confounding. Model criticism and testing. Computational methods for model fitting, including Bayesian approaches.
Prerequisite: STATS 210 and 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
2877
STATS 763
: Advanced Regression Methodology2020 Semester One (1203)
Generalised linear models, generalised additive models, survival analysis. Smoothing and semiparametric regression. Marginal and conditional models for correlated data. Model selection for prediction and for control of confounding. Model criticism and testing. Computational methods for model fitting, including Bayesian approaches.
No pre-requisites or restrictions
2878
STATS 765
: Statistical Learning for Data Science2024 Semester One (1243)
Concepts of modern predictive modelling and machine learning such as loss functions, overfitting, generalisation, regularisation, sparsity. Techniques including regression, recursive partitioning, boosting, neural networks. Application to real data sets from a variety of sources, including data quality assessment, data preparation and reporting.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208 and 15 points from STATS 210, 225, 707
Corequisite: May be taken with STATS 707
Restriction: STATS 369
Restriction: STATS 369
2879
STATS 765
: Statistical Learning for Data Science2023 Semester One (1233)
Concepts of modern predictive modelling and machine learning such as loss functions, overfitting, generalisation, regularisation, sparsity. Techniques including regression, recursive partitioning, boosting, neural networks. Application to real data sets from a variety of sources, including data quality assessment, data preparation and reporting.
Prerequisite: 15 points from STATS 201 or 207 or 208 and 15 points from STATS 210 or 225, or STATS 707
Corequisite: May be taken with STATS 707
Restriction: STATS 369
Restriction: STATS 369
2880
STATS 765
: Statistical Learning for Data Science2022 Semester One (1223)
Concepts of modern predictive modelling and machine learning such as loss functions, overfitting, generalisation, regularisation, sparsity. Techniques including regression, recursive partitioning, boosting, neural networks. Application to real data sets from a variety of sources, including data quality assessment, data preparation and reporting.
Prerequisite: 15 points from STATS 201 or 207 or 208 and 15 points from STATS 210 or 225, or STATS 707
Corequisite: May be taken with STATS 707
Restriction: STATS 369
Restriction: STATS 369
2881
STATS 765
: Statistical Learning for Data Science2021 Semester One (1213)
Concepts of modern predictive modelling and machine learning such as loss functions, overfitting, generalisation, regularisation, sparsity. Techniques including regression, recursive partitioning, boosting, neural networks. Application to real data sets from a variety of sources, including data quality assessment, data preparation and reporting.
Prerequisite: 15 points from STATS 201 or 207 or 208 and 15 points from STATS 210 or 225, or STATS 707
Corequisite: May be taken with STATS 707
Restriction: STATS 369
Restriction: STATS 369
2882
STATS 765
: Statistical Learning for Data Science2020 Semester One (1203)
Concepts of modern predictive modelling and machine learning such as loss functions, overfitting, generalisation, regularisation, sparsity. Techniques including regression, recursive partitioning, boosting, neural networks. Application to real data sets from a variety of sources, including data quality assessment, data preparation and reporting.
Prerequisite: 15 points from STATS 201 or 207 or 208 and 15 points from STATS 210 or 225, or STATS 707
Corequisite: May be taken with STATS 707
Restriction: STATS 369
Restriction: STATS 369
2883
STATS 766
: Multivariate Analysis2023 Semester Two (1235)
A selection of topics from multivariate analysis, including: advanced methods of data display (e.g., Correspondence and Canonical Correspondence Analysis, Biplots, and PREFMAP) and an introduction to classification methods (e.g., various types of Discriminant Function Analysis).
Prerequisite: STATS 310 or 732
2884
STATS 766
: Multivariate Analysis2022 Semester Two (1225)
A selection of topics from multivariate analysis, including: advanced methods of data display (e.g., Correspondence and Canonical Correspondence Analysis, Biplots, and PREFMAP) and an introduction to classification methods (e.g., various types of Discriminant Function Analysis).
Prerequisite: STATS 302 or 767
2885
STATS 767
: Foundations of Applied Multivariate Analysis2024 Semester One (1243)
Fundamentals of exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208, 707
Restriction: STATS 302
Restriction: STATS 302
2886
STATS 767
: Foundations of Applied Multivariate Analysis2023 Semester One (1233)
Fundamentals of exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 707
Restriction: STATS 302
Restriction: STATS 302
2887
STATS 767
: Foundations of Applied Multivariate Analysis2022 Semester One (1223)
Fundamentals of exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 707
Restriction: STATS 302
Restriction: STATS 302
2888
STATS 767
: Foundations of Applied Multivariate Analysis2021 Semester One (1213)
Fundamentals of exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 707
Restriction: STATS 302
Restriction: STATS 302
2889
STATS 767
: Foundations of Applied Multivariate Analysis2020 Semester One (1203)
Fundamentals of exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208
Restriction: STATS 302
Restriction: STATS 302
2890
STATS 768
: Longitudinal Data Analysis2024 Semester Two (1245)
Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208, 210, 707
2891
STATS 768
: Longitudinal Data Analysis2023 Semester Two (1235)
Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 210, 707
2892
STATS 768
: Longitudinal Data Analysis2021 Semester Two (1215)
Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 210, 707
2893
STATS 768
: Longitudinal Data Analysis2020 Semester Two (1205)
Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.
No pre-requisites or restrictions
2894
STATS 769
: Advanced Data Science Practice2024 Semester Two (1245)
Databases, SQL, scripting, distributed computation, other data technologies.
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from ENGSCI 314, STATS 201, 207, 208, 707
2895
STATS 769
: Advanced Data Science Practice2023 Semester Two (1235)
Databases, SQL, scripting, distributed computation, other data technologies.
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from BIOSCI 209, STATS 201, 207, 208, 707
2896
STATS 769
: Advanced Data Science Practice2022 Semester Two (1225)
Databases, SQL, scripting, distributed computation, other data technologies.
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from BIOSCI 209, STATS 201, 207, 208, 707
2897
STATS 769
: Advanced Data Science Practice2021 Semester Two (1215)
Databases, SQL, scripting, distributed computation, other data technologies.
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from BIOSCI 209, STATS 201, 207, 208, 707
2898
STATS 769
: Advanced Data Science Practice2020 Semester Two (1205)
Databases, SQL, scripting, distributed computation, other data technologies.
No pre-requisites or restrictions
2899
STATS 770
: Introduction to Medical Statistics2024 Semester One (1243)
An introduction to ideas of importance in medical statistics, such as measures of risk, basic types of medical study, causation, ethical issues and censoring, together with a review of common methodologies.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208 and 15 points from STATS 210, 225, 707
2900
STATS 770
: Introduction to Medical Statistics2023 Semester One (1233)
An introduction to ideas of importance in medical statistics, such as measures of risk, basic types of medical study, causation, ethical issues and censoring, together with a review of common methodologies.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208 and 15 points from STATS 210, 225, 707
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