Artificial Neural Networks, Machine Learning and Deep Thinking Training Course

1. Understanding classification using nearest neighbors 

• 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 
• The 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 
• Visualizing performance tradeoffs 
• 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 
• Customizing 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