Search Course Outline
Showing 25 course outlines from 2938 matches
2276
STATS 760
: A Survey of Modern Applied Statistics2020 Semester One (1203)
A survey of techniques from modern applied statistics. Topics covered will be linear, non-linear and generalised linear models, modern regression including CART and neural networks, mixed models, survival analysis, time series and spatial statistics.
Prerequisite: STATS 310, 330
2277
STATS 762
: Regression for Data Science2023 Semester One (1233)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
Restriction: STATS 330
2278
STATS 762
: Regression for Data Science2022 Semester One (1223)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
Restriction: STATS 330
2279
STATS 762
: Regression for Data Science2021 Semester One (1213)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
Restriction: STATS 330
2280
STATS 762
: Regression for Data Science2020 Semester One (1203)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
Restriction: STATS 330
2281
STATS 763
: Advanced Regression Methodology2023 Semester Two (1235)
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 or 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
2282
STATS 763
: Advanced Regression Methodology2022 Semester One (1223)
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 or 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
2283
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
2284
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
2285
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
2286
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
2287
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
2288
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
2289
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
2290
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
2291
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
2292
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
2293
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
2294
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
2295
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
2296
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
2297
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
2298
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
2299
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
2300
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
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118