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Course Outline
1. Understanding classification using nearest neighbours
- The kNN algorithm
- Calculating distance
- Choosing an appropriate k
- Preparing data for use with kNN
- Why is the kNN algorithm lazy?
2. Understanding Naive Bayes
- Basic concepts of Bayesian methods
- Probability
- Joint probability
- Conditional probability with Bayes' theorem
- The Naive Bayes algorithm
- Naive Bayes classification
- The Laplace estimator
- Using numeric features with Naive Bayes
3. Understanding decision trees
- Divide and conquer
- The C5.0 decision tree algorithm
- Choosing the best split
- Pruning the decision tree
4. Understanding classification rules
- Separate and conquer
- The One Rule algorithm
- The RIPPER algorithm
- Rules from decision trees
5. Understanding regression
- Simple linear regression
- Ordinary least squares estimation
- Correlations
- Multiple linear regression
6. Understanding regression trees and model trees
- Adding regression to trees
7. Understanding neural networks
- From biological to artificial neurons
- Activation functions
- Network topology
- The number of layers
- The direction of information travel
- The number of nodes in each layer
- Training neural networks with backpropagation
8. Understanding Support Vector Machines
- Classification with hyperplanes
- Finding the maximum margin
- The case of linearly separable data
- The case of non-linearly separable data
- Using kernels for non-linear spaces
9. Understanding association rules
- The Apriori algorithm for association rule learning
- Measuring rule interest – support and confidence
- Building a set of rules with the Apriori principle
10. Understanding clustering
- Clustering as a machine learning task
- The k-means algorithm for clustering
- Using distance to assign and update clusters
- Choosing the appropriate number of clusters
11. Measuring performance for classification
- Working with classification prediction data
- A closer look at confusion matrices
- Using confusion matrices to measure performance
- Beyond accuracy – other measures of performance
- The kappa statistic
- Sensitivity and specificity
- Precision and recall
- The F-measure
- Visualising performance trade-offs
- ROC curves
- Estimating future performance
- The holdout method
- Cross-validation
- Bootstrap sampling
12. Tuning stock models for better performance
- Using caret for automated parameter tuning
- Creating a simple tuned model
- Customising the tuning process
- Improving model performance with meta-learning
- Understanding ensembles
- Bagging
- Boosting
- Random forests
- Training random forests
- Evaluating random forest performance
13. Deep Learning
- Three Classes of Deep Learning
- Deep Autoencoders
- Pre-trained Deep Neural Networks
- Deep Stacking Networks
14. Discussion of Specific Application Areas
21 Hours
Testimonials (1)
Very flexible.
