This class was created by Brainscape user Dan McArthur. Visit their profile to learn more about the creator.

Decks in this class (9)

Subject Information
Overview
1  cards
L1: Introduction to Data Analysis
After working your way through this week, you should be able to: - Differentiate between supervised and unsupervised learning - Differentiate between inference and prediction - Understand the trade-off between prediction accuracy and interpretability - Understand the bias-variance trade-off
20  cards
L2: Exploratory Data Analysis
After this deck you should be able to: - differentiate discrete and continuous variables - understand the basic statistics used to characterise distributions - produce proper exploratory data analysis on different types of data via different R packages
14  cards
L3: Linear Regression
After this week: - Understand how regression analysis works - Apply linear models to solving different regression problems - Critically assess the accuracy of coefficient estimates and the accuracy of the model - Produce a precise analysis of the model output
22  cards
L4: Classification Tasks
After this deck: - Apply linear models to solving different classification problems; - Assess the accuracy of coefficient estimates and the accuracy of the model; - Produce analysis on the model output.;
15  cards
L5: Resampling Methods
- Understand the principle of CV and Bootstrap - Apply cross-validation methods to estimate the test error associated with the learning method, and improving the estimates. - Apply the bootstrap to quantifying the uncertainty associated with a given estimate or a learning model
6  cards
L6: Model Selection and Regularisation
- Understand the algorithms, such as stepwise selection, ridge/lasso regression - Apply those algorithms to further improve the modelling accuracy. Unit learning outcomes: - Evaluate the limitations, appropriateness and benefits of data analytics methods for given tasks; - Design solutions to real world problems with data analytics techniques;
23  cards
L7: Non-Linear Regression
After working your way through this module, you should be able to: - Explain the limits and constraints of nonlinear methods - Implement and assess different nonlinear models, such as splines, additive models, etc.
12  cards
L8: Tree Based Methods
- Differentiate between tree-based methods and the other methods - Understand the advantages and disadvantages of trees - Generate more powerful prediction model with bagging, random forest and boosting.
12  cards

More about
FIT5149

  • Class purpose General learning

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