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Weight Initialization and Activation Functions Long Short Term Memory Neural Networks (LSTM)įully-connected Overcomplete Autoencoder (AE)įorward- and Backward-propagation and Gradient Descent (From Scratch FNN Regression)įrom Scratch Logistic Regression Classification ?Mathematical modeling-evolutionary ecology, evolutionary game theory, inclusive fitness, structured populations.Building a Linear Regression Model with PyTorchīuilding a Linear Regression Model with PyTorch (GPU) Cross-appointed to Biology and Education) ?Statistical population models-generalized additive time-series models (splines) fit to multinomial count data, nonlinear population models, resampling methods Mathematical modeling-stage-structured population models. Software: JMP, R, DataGraph, Raven, Veusz ?Analysis of animal and plant colours, paternity, and bird song. Univariate and multivariate statisitics, resampling, exploratory data analysis, data management. Information Theoretic approaches, Generalized Linear Models, data vizualization. ?Generalized linear models, mixed models - analysis of geographic variation in richness, rates of trait evolution, neutral genetic divergence, species accumulation curves, spatial overlap in distributions, behavioural experiments (song playback, mate choice), quantifying natural history, habitat use moving into Information Theoretic approach Software: MrBayes, PAUP*, Mesquite, Structure, BEAST, JMP univariate and multivariate analyses of spatial genetic structure-Bayesian assignment methods ?Phylogenetics-Bayesian, likelihood and parsimony approaches to phylogeny inference, character mapping, constraint analyses, coalescence Generalized linear models-morphometric and sonographic analyses. ?Speciation and diversification of vertebrates Software: Arlequin, BayesAss, Bottleneck, Fluctuate, GeoDis, Mega, Migrate, 2MOD, Populations, Structure, TCS ?coalescent theory, molecular assignments ?Experimental design, generalized linear models, quantitative genetics and QTL mapping
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?Experimental design linear models: including mixed models, repeated-measures models, analysis of covariance, multiple regression multivariate analysis of morphological and environmental data resampling and randomization methods computer simulation spatial analysis of geographic variation quantitative-genetic analyses estimating mating systems using likelihood-based analysis of molecular progeny array data. Constraint-based metabolic modelling (flux balance analysis), phylogenetics, statistics Prokaryotic gene function, metabolism, and genome evolution ?Mathematical modeling-evolutionary ecology, infectious disease evolution, kin selection, sexual selection, epigenetic inheritance, inter- and intra-specific interactions. ?regression (GLM) multivariate techniques to assess and reconstruct environmental change from community data (e.g., regression and calibration (WA, ML, PLS, Neural Networks) unconstrained (e.g., PCA, CA, DCA, NMDS), constrained, and partial ordination techniques (e.g., RDA, CCA, variance partitioning) multivariate assessment of difference between groups (e.g., ANOSIM, SIMPER) clustering techniques ?Analysis of variance, quantitative genetics of life histories, heritability, generalized linear models ?Evolutionary genetics and organismal biology ?Analysis of variance-multifactor repeated measures ANOVA on experimental data Ordination-principal component analysis of zooplankton community data, redundancy analysis to relate environmental gradients to zooplankton community data.
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? Environmental impacts on aquatic communities and ecosystems We are a diverse group of Biology faculty who use quantitative skills in our everyday research, and are interested in developing these skills for undergraduate, graduate and postgraduate training.