Course Outline

Machine Learning Algorithms in Julia

Introductory concepts

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

Linear & logistic regression

(NaiveBayes & GLM)

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

Distances

  • What is a distance?
  • Euclidean
  • Cityblock
  • Cosine
  • Correlation
  • Mahalanobis
  • Hamming
  • MAD
  • RMS
  • Mean squared deviation

Dimensionality reduction

  • Principal Component Analysis (PCA)
    • Linear PCA
    • Kernel PCA
    • Probabilistic PCA
    • Independent CA
  • Multidimensional scaling

Altered regression methods

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

Clustering

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

Standard machine learning models

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

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

Artificial neural networks

(Flux package)

  • Stochastic gradient descent & strategies
  • Multilayer perceptrons forward feed & back propagation
  • Regularization
  • Recurrence neural networks (RNN)
  • Convolutional neural networks (Convnets)
  • Autoencoders
  • Hyperparameters

Requirements

This course is intended for people that already have a background in data science and statistics.

 21 Hours

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