Machine Learning with R

• Data scientists
• Programmers
• Business analysts 
• Engineers
• Scientists

Leading Training's Introduction to R Programming or equivalent knowledge

Introduction to machine learning

• Notation
• An example
• Exercises
• Evaluation metrics
  • Training and test sets
  • Overall accuracy
  • The confusion matrix
  • Sensitivity and specificity
  • Balanced accuracy and F1 score
  • Prevalence matters in practice
  • ROC and precision-recall curves
  • The loss function
• Exercises
• Conditional probabilities and expectations
  • Conditional probabilities
  • Conditional expectations
  • Conditional expectation minimizes squared loss function
• Exercises
• Case study: is it a 2 or a 7?

Smoothing 

• Bin smoothing
• Kernels
• Local weighted regression (loess)
  • Fitting parabolas
  • Beware of default smoothing parameters
• Connecting smoothing to machine learning
• Exercises

 Cross validation 

• Motivation with k-nearest neighbors
  • Over-training
  • Over-smoothing
  • Picking the k in kNN
• Mathematical description of cross validation
• K-fold cross validation
• Exercises
• Bootstrap
• Exercises

The caret package 

• The caret train functon
• Cross validation
• Example: fitting with loess

Examples of algorithms 

• Linear regression
  • The predict function
• Exercises
• Logistic regression
  • Generalized linear models
  • Logistic regression with more than one predictor
• Exercises
• k-nearest neighbors
• Exercises
• Generative models
  • Naive Bayes
  • Controlling prevalence
  • Quadratic discriminant analysis
  • Linear discriminant analysis
  • Connection to distance
• Case study: more than three classes
• Exercises
• Classification and regression trees (CART)
  • The curse of dimensionality
  • CART motivation
  • Regression trees
  • Classification (decision) trees
• Random forests
• Exercises
• Machine learning in practice 
• Preprocessing
• k-nearest neighbor and random forest
• Variable importance
• Visual assessments
• Ensembles
• Exercises
• Large datasets 
  • Matrix algebra
  • Notation
  • Converting a vector to a matrix
  • Row and column summaries
  • apply
  • Filtering columns based on summaries
  • Indexing with matrices
  • Binarizing the data
  • Vectorization for matrices
  • Matrix algebra operations
• Exercises
• Distance
  • Euclidean distance
  • Distance in higher dimensions
  • Euclidean distance example
  • Predictor space
  • Distance between predictors
• Exercises
• Dimension reduction
  • Preserving distance
  • Linear transformations (advanced)
  • Orthogonal transformations (advanced)
  • Principal component analysis
  • Iris example
  • MNIST example
• Exercises
• Recommendation systems
  • Movielens data
  • Recommendation systems as a machine learning challenge
  • Loss function
  • A first model
  • Modeling movie effects
  • User effects
• Exercises
• Regularization
  • Motivation
  • Penalized least squares
  • Choosing the penalty terms
• Exercises
• Matrix factorization
  • Factors analysis
  • Connection to SVD and PCA
• Exercises
• 34 Clustering 
• Hierarchical clustering
• k-means
• Heatmaps
• Filtering features
• Exercises

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