Artificial Neural Networks, Machine Learning and Deep Thinking

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?

• 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

• Divide and conquer
• The C5.0 decision tree algorithm
• Choosing the best split
• Pruning the decision tree

• Separate and conquer
• The One Rule algorithm
• The RIPPER algorithm
• Rules from decision trees

• Simple linear regression
• Ordinary least squares estimation
• Correlations
• Multiple linear regression

• Adding regression to trees

• 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

• 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

• The Apriori algorithm for association rule learning
• Measuring rule interest – support and confidence
• Building a set of rules with the Apriori principle

• 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

• 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

• 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

• Three Classes of Deep Learning
• Deep Autoencoders
• Pre-trained Deep Neural Networks
• Deep Stacking Networks