Machine Learning Algorithms in Julia Training Course

Machine Learning Algorithms in Julia for Government

Introductory Concepts

• Supervised and unsupervised learning
• Cross-validation and model selection
• Bias/variance tradeoff

Linear and Logistic Regression for Government

(NaiveBayes and GLM)

• Introductory concepts
• Fitting linear regression models
• Model diagnostics
• Naive Bayes
• Fitting a logistic regression model
• Model diagnostics
• Model selection methods

Distances for Government

• What is a distance?
• Euclidean
• Cityblock
• Cosine
• Correlation
• Mahalanobis
• Hamming
• Mean absolute deviation (MAD)
• Root mean squared (RMS)
• Mean squared deviation

Dimensionality Reduction for Government

• Principal Component Analysis (PCA)
  • Linear PCA
  • Kernel PCA
  • Probabilistic PCA
  • Independent component analysis (ICA)
• Multidimensional scaling

Altered Regression Methods for Government

• Basic concepts of regularization
• Ridge regression
• Lasso regression
• Principal component regression (PCR)

Clustering for Government

• K-means
• K-medoids
• DBSCAN
• Hierarchical clustering
• Markov Cluster Algorithm
• Fuzzy C-means clustering

Standard Machine Learning Models for Government

(NearestNeighbors, DecisionTree, LightGBM, XGBoost, EvoTrees, LIBSVM packages)

• Gradient boosting concepts
• K nearest neighbors (KNN)
• Decision tree models
• Random forest models
• XGBoost
• EvoTrees
• Support vector machines (SVM)

Artificial Neural Networks for Government

(Flux package)

• Stochastic gradient descent and strategies
• Multilayer perceptrons: forward feed and back propagation
• Regularization techniques
• Recurrent neural networks (RNN)
• Convolutional neural networks (ConvNets)
• Autoencoders
• Hyperparameters optimization