Course Outline

• Introduction
  • Instructor team
  • Our Machine Learning Publications
  • Software
  • R/Python tutorials
  • Data sets
  • Disclaimer
• Introduction to Machine Learning
  • Machine Learning
  • Machine Learning Examples
  • Machine Learning Process Model
  • Types of Machine Learning
  • Quiz
• Data Preprocessing
  • Motivation
  • Types of data
  • Types of variables
  • Denormalizing data
  • Sampling
    • Sampling in R
    • Sampling in Python
  • Visual data exploration
    • Visual data exploration in R
    • Visual data exploration in Python
  • Descriptive statistics
  • Missing values
    • Missing values in R
    • Missing values in Python
  • Outliers
    • Outliers in R
    • Outliers in Python
  • Categorization
    • Categorization in R
    • Categorization in Python
  • WOE and IV
    • WOE and IV in R
    • WOE and IV in Python
  • Quiz
• Feature Engineering
  • Feature Engineering Defined
  • RFM features
  • Trend features
  • Logarithmic transformation
  • Power transformation
  • Box-Cox transformation
    • Box-Cox transformation in R
    • Box-Cox transformation in Python
  • Yeo-Johnson transformation
  • Performance Optimization
    • Performance Optimisation with Yeo Johnson transformation in R
    • Performance Optimisation with Yeo Johnson transformation in Python
  • Principal Component Analysis
  • t-SNE
  • Quiz
• Regression
  • Linear Regression
    • Linear Regression in R
    • Linear Regression in Python
  • High Dimensional Data
  • Ridge Regression
    • Ridge Regression in R
    • Ridge Regression in Python
  • LASSO Regression
    • LASSO Regression in R
    • LASSO Regression in Python
  • Elastic Net
    • Elastic net in R
    • Elastic net in Python
  • Principal Component Regression
  • Partial Least Squares (PLS) regression
  • Generalized Linear Models (GLMs)
  • Generalized Additive Models (GAMs)
• Classification
  • Linear Regression
  • Logistic Regression
    • Logistic Regression in R
    • Logistic Regression in Python
  • Nomograms
    • Nomograms in R
  • Decision trees
    • Decision trees in R
    • Decision trees in Python
  • K-nearest neighbor
    • K-nearest neighbor in R
    • K-nearest neighbor in Python
  • Multiclass classification
  • One versus One coding
  • One versus All coding coding
  • Multiclass decision trees
• Measuring the performance of predictive analytics techniques
  • Performance measurement
  • Split sample method
  • Cross-validation
  • Single sample method
  • Performance measures for classification
  • Confusion matrix (classification accuracy, classification error, sensitivity, specificity)
  • ROC curve and area under ROC curve
    • ROC curve in R
    • ROC curve in Python
  • CAP curve and Accuracy Ratio
  • Lift curve
  • Kolmogorov-Smirnov distance
  • Mahalanobis distance
  • Performance measures for regression
  • Quiz
• Association and Sequence Rules
  • Association Rules
  • Support and Confidence
  • Association rule mining
    • Association rule mining in R
    • Association rule mining in Python
  • Lift
  • Association rule extensions
  • Post-Processing Association Rules
  • Association rules applications
  • Sequence rules
  • Quiz
• Clustering Techiques
  • Hierharchical clustering
    • Hierarchical clustering in R
    • Hierarchical clustering in Python
  • K-means clustering
    • K-means clustering in R
    • K-means clustering in Python
  • DBSCAN
    • DBSCAN in R
    • DBSCAN in Python
  • Evaluating clustering solutions
  • Quiz
• Neural Networks
  • Neural Networks
    • Neural Networks in R
    • Neural Networks in Python
  • Deep Learning Neural Networks
  • Opening Neural Network Black Box
  • Variable Selection
  • Rule Extraction
  • Decompositional Rule Extraction
  • Pedagogical Rule Extraction
  • Quality of Extracted Rule Set
  • Rule Extraction Example
  • Two-Stage Model
  • Self-Organizing Maps
    • SOMs in R
  • Self-Organizing Maps Example
  • Self-Organizing Maps Evaluated
  • Quiz
• Support Vector Machines (SVMs)
  • Problems with neural networks
  • Linear programming
  • Linear Separable case
  • Linear non-separable case
  • Non linear SVM classifier
    • RBF SVM in R
    • RBF SVM in Python
  • Kernel functions
  • Neural Network Interpretation of SVM classifier
  • Tuning the hyperparameters
    • Tuning the hyperparameters of an RBF SVM in R
    • Tuning the hyperparameters of an RBF SVM in Python
  • Benchmarking study
  • SVMs for regression
  • One-class SVMs 
    • One-class SVM in R
    • One-class SVM in Python
  • Extensions to SVMs
  • Opening the SVM black box
  • Quiz
• Ensemble Methods
  • Ensemble methods
  • Bootstrapping
  • Bagging
    • Baggin in R
    • Bagging in Python
  • Boosting
    • Adaboost in R
    • Adaboost in Python
  • Random Forests
    • Random Forests in R
    • Random Forests in Python
  • XGBoost
    • XGBoost in R
    • XGBoost in Python
  • Quiz
• Bayesian Networks
  • Bayesian Networks
  • Example Bayesian Network Classifier
  • Naive Bayes Classifier
    • Naive Bayes classifier in R
    • Naive Bayes classifier in Python
  • Tree Augmented Naive Bayes Classifiers
  • Bayesian networks examples
  • Quiz
• Variable Selection
  • Variable selection
  • Filter methods (gain, Cramer’s V, Fisher score)
    • Cramer’s V in R
    • Cramer’s V in Python
    • Information Value in R
    • Information Value in Python
  • Forward/Backward/Stepwise regression
    • Forward/Backward/Stepwise in R
  • BART: Backward Regression Trimming
    • BART variable selection in R
  • Criteria for variable selection
  • Quiz
• Model interpretation
  • Model interpretation
  • Feature Importance
  • Permutation based feature importance
  • Partial dependence plots
    • Partial dependence plots in Python
  • Individual conditional expectation (ICE) plots
    • ICE plots in Python
  • Visual analytics
  • Decision tables
  • LIME
    • LIME in Python
  • Shapley value
    • Shapley value in Python
• Model deployment
  • Model deployment
  • Model governance
  • Model ethics
  • Model documentation
  • Model backtesting
  • Model benchmarking
  • Model stress testing
  • Privacy and Security
  • Quiz
• Machine Learning Pitfalls
  • Sample bias
  • Model risk
  • Deep everything
  • Leader versus follower
  • Complexity versus trust
  • Statistical myopia
  • Profit Driven Machine Learning
  • Quiz
• Quiz