This class was created by Brainscape user Dan McArthur. Visit their profile to learn more about the creator.
By:
Dan McArthur
- decks
- flashcards
- learners
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
Learn faster with Brainscape on your web, iPhone, or Android device. Study Dan McArthur's FIT5149 flashcards now!
How studying works.
Brainscape's adaptive web mobile flashcards system will drill you on your weaknesses, using a pattern guaranteed to help you learn more in less time.
Add your own flashcards.
Either request "Edit" access from the author, or make a copy of the class to edit as your own. And you can always create a totally new class of your own too!
What's Brainscape anyway?
Brainscape is a digital flashcards platform where you can find, create, share, and study any subject on the planet.
We use an adaptive study algorithm that is proven to help you learn faster and remember longer....
