This class was created by Brainscape user Dan McArthur. Visit their profile to learn more about the creator.
By:
Dan McArthur
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Decks in this class (9)
Subject Information
No deck description has yet been added by the author.
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
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