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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
