Machine Learning

Course Content

Total learning: 4 lessons

Contents

  1. Regression Algorithms
  • Ordinary Least Squares Regression (OLSR)
  • Linear Regression
  • Logistic Regression
  • Stepwise Regression
  • Forward Regression 
  • Backward regression 
  • Multivariate Adaptive Regression Splines (MARS)
  • Locally Estimated Scatterplot Smoothing (LOESS)
  1. Instance-based Algorithms
  • k-Nearest Neighbour (kNN)
  • Learning Vector Quantization (LVQ)
  • Self-Organizing Map (SOM)
  • Locally Weighted Learning (LWL)
  1. Regularization Algorithms
  • Ridge Regression
  • Least Absolute Shrinkage and Selection Operator (LASSO)
  • Elastic Net
  • Least-Angle Regression (LARS)
  1. Decision Tree Algorithms
  • Classification and Regression Tree (CART)
  • Iterative Dichotomiser 3 (ID3)
  • C4.5 and C5.0 (different versions of a powerful approach)
  • Chi-squared Automatic Interaction Detection (CHAID)
  • Decision Stump
  • M5
  • Conditional Decision Trees
  1. Bayesian Algorithms
  • Naive Bayes
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • Averaged One-Dependence Estimators (AODE)
  • Bayesian Belief Network (BBN)
  • Bayesian Network (BN)
  1. Clustering Algorithms
  • k-Means
  • k-Medians
  • Expectation Maximisation (EM)
  • Hierarchical Clustering
  1. Association Rule Learning Algorithms
  • Apriori algorithm
  • Eclat algorithm
  1. Artificial Neural Network Algorithms
  • Perceptron
  • Back-Propagation
  • Hopfield Network
  • Radial Basis Function Network (RBFN)
  1. Deep Learning Algorithms
  • Deep Boltzmann Machine (DBM)
  • Deep Belief Networks (DBN)
  • Convolutional Neural Network (CNN)
  • Stacked Auto-Encoders
  1. Dimensionality Reduction Algorithms
  • Principal Component Analysis (PCA)
  • Principal Component Regression (PCR)
  • Partial Least Squares Regression (PLSR)
  • Sammon Mapping
  • Multidimensional Scaling (MDS)
  • Projection Pursuit
  • Linear Discriminant Analysis (LDA)
  • Mixture Discriminant Analysis (MDA)
  • Quadratic Discriminant Analysis (QDA)
  • Flexible Discriminant Analysis (FDA)
  1. Ensemble Algorithms
  • Boosting
  • Bootstrapped Aggregation (Bagging)
  • AdaBoost
  • Stacked Generalization (blending)
  • Gradient Boosting Machines (GBM)
  • Gradient Boosted Regression Trees (GBRT)
  • Random Forest
  1. Other Algorithms
  • Computational intelligence (evolutionary algorithms, etc.)
  • Computer Vision (CV)
  • Natural Language Processing (NLP)
  • Recommender Systems
  • Reinforcement Learning
  • Graphical Models
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