Machine Learning Algorithms in Julia

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

This instructor-led, live training (online or onsite) is aimed at people that already have a background in data science and statistics. 

Format of the Course

• Interactive lecture and discussion.
• Exercises and practice.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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