• • INTRODUCTION TO MACHINE LEARNING
  • The origins of machine learning
  • Uses and abuses of machine learning
  • Machine learning successes
  • The limits of machine learning
  • Machine learning ethics
  • How machines learn
  • Data storage
  • Abstraction
  • Generalization
  • Evaluation
  • Machine learning in practice
  • Types of input data
  • Types of machine learning algorithms
  • Matching input data to algorithms

   

   

   

   

   

  • Regression Algorithms

   

  • The most popular regression algorithms are:
  • Ordinary Least Squares Regression (OLSR)
  • Linear Regression
  • Logistic Regression
  • Stepwise Regression

   

   

   

   

  • Instance-based Algorithms

   

  • The most popular instance-based algorithms are:
  • k-Nearest Neighbor (kNN)
  • Learning Vector Quantization (LVQ)
  • Self-Organizing Map (SOM)
  • Locally Weighted Learning (LWL)
  • Support Vector Machines (SVM)

   

   

  • Regularization Algorithms

   

  • The most popular regularization algorithms are:
  • Ridge Regression
  • Least Absolute Shrinkage and Selection Operator (LASSO)
  • Elastic Net

   

   

  • Decision Tree Algorithms

   

  • The most popular decision tree algorithms are:
  • Classification and Regression Tree (CART)
  • Iterative Dichotomiser 3 (ID3)
  • 5 and C5.0 (different versions of a powerful approach)
  • Chi-squared Automatic Interaction Detection (CHAID)
  • Decision Stump
  • M5
  • Conditional Decision Trees

   

   

   

  • Bayesian Algorithms

   

  • The most popular Bayesian algorithms are:
  • Naive Bayes
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • Averaged One-Dependence Estimators (AODE)
  • Bayesian Belief Network (BBN)
  • Bayesian Network (BN)

   

   

   

  • Clustering Algorithms

   

  • The most popular clustering algorithms are:
  • k-Means
  • k-Medians
  • Expectation Maximisation (EM)
  • Hierarchical Clustering
  • Divide and Conquer – Classification Using Decision Trees and Rules
  • Understanding decision trees
  • Divide and conquer
  • The decision tree algorithm
  • Choosing the best split
  • Pruning the decision tree
  • Example – identifying risky bank loans using C5.0 decision trees

   

  • Forecasting Numeric Data – Regression Methods
  • Understanding regression
  • Simple linear regression
  • Ordinary least squares estimation
  • Correlations
  • Multiple linear regression
  • Example – predicting medical expenses using linear regression

   

  • Black Box Methods – Neural Networks and Support Vector Machines
  • 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
  • Example – Modeling the strength of concrete with ANNs

   

  • Finding Groups of Data – Clustering with k-means
  • Understanding clustering
  • Clustering as a machine learning task
  • The k-means clustering algorithm
  • Using distance to assign and update clusters
  • Choosing the appropriate number of clusters
  • Example – finding teen market segments using k-means clustering

   

  • Finding Patterns – Market Basket Analysis Using Association Rules
  • 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
  • Example – identifying frequently purchased groceries with
  • Association rules

Duration 24 Hours