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
Automatic Differentiation with Zygote.jl
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- Automatic Differentiation in Machine Learning: A Survey, Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind, 2018 Journal of Machine Learning Research, Vol. 18 (JMLR) - Offers a comprehensive review of automatic differentiation, its various modes, and its importance for contemporary machine learning, including comparisons with numerical and symbolic methods.
- Zygote.jl Documentation, The Zygote.jl Developers, 2024 (FluxML) - The official source for Zygote.jl, detailing its source-to-source AD mechanism, usage, and features for differentiable programming in Julia.
- Flux.jl Documentation, The Flux.jl Developers, 2025 (FluxML) - Provides information on how Zygote.jl is used within the Flux.jl framework for building and training deep learning models, especially regarding parameter management and gradient calculations.