Julia for Machine Learning
Chapter 1: Getting Started with Julia for Machine Learning
Why Julia for Machine Learning? Strengths and Comparisons
Setting Up Your Julia Machine Learning Environment
Julia's Type System and Multiple Dispatch for Scientific Computing
Essential Julia Syntax for Data Operations
Working with Arrays and Matrices in Julia
Introduction to DataFrames.jl
Package Management with Pkg.jl
Hands-on practical: Environment Setup and Basic Data Operations
Chapter 2: Data Manipulation and Preparation in Julia
Loading and Saving Data with DataFrames.jl
Data Cleaning: Handling Missing Values and Outliers
Data Transformation: Scaling, Encoding, and Binning
Feature Engineering Principles
Applying Feature Engineering in Julia
Data Visualization with Plots.jl and Makie.jl
Hands-on practical: Data Cleaning and Feature Creation
Chapter 3: Implementing Supervised Learning Algorithms
Overview of the MLJ.jl Ecosystem
Building and Training Linear Models with MLJ.jl
Implementing Decision Trees and Ensemble Methods
Support Vector Machines (SVMs) using Julia Packages
Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score
Cross-Validation and Hyperparameter Tuning in MLJ.jl
Hands-on practical: Training and Evaluating Supervised Models
Chapter 4: Unsupervised Learning and Dimensionality Reduction
Clustering with K-Means in Julia
Density-Based Clustering: DBSCAN
Principal Component Analysis (PCA) for Dimensionality Reduction
Other Dimensionality Reduction Techniques
Evaluating Clustering Performance
Hands-on practical: Applying Clustering and PCA
Chapter 5: Building Machine Learning Pipelines and Workflows
Introduction to MLJ Pipelines
Constructing Preprocessing and Model Pipelines
Saving and Loading Trained Models and Pipelines
Composing and Tuning Complex Workflows
Strategies for Reproducible ML Experiments in Julia
Hands-on practical: Creating and Managing ML Pipelines
Chapter 6: Introduction to Deep Learning with Flux.jl
Fundamentals of Neural Networks
Getting Started with Flux.jl: Tensors and Layers
Building Feedforward Neural Networks
Defining Loss Functions and Optimizers
Training Neural Networks in Flux.jl
Automatic Differentiation with Zygote.jl
Working with Gradients
Hands-on practical: Building and Training a Simple Neural Network
Principal Component Analysis (PCA) for Dimensionality Reduction
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- Analysis of a complex of statistical variables into principal components, Harold Hotelling, 1933 Journal of Educational Psychology, Vol. 24 (American Psychological Association) DOI: 10.1037/h0071325 - This seminal paper introduced the method of principal component analysis as a tool for reducing the dimensionality of multivariate data.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009 (Springer) - A classic textbook covering a wide range of statistical learning methods, with detailed chapters on dimensionality reduction techniques including PCA (2nd edition).
- Principal Component Analysis (PCA) in MultivariateStats.jl, JuliaStats contributors, 2024 (JuliaStats) - The official documentation for the Julia
MultivariateStats.jlpackage, providing detailed usage examples and API reference for PCA implementation. - Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, 2012 (The MIT Press) - This comprehensive textbook provides a deep theoretical treatment of machine learning, including a thorough mathematical and probabilistic discussion of PCA.