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
Defining Loss Functions and Optimizers
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook covering fundamental concepts of deep learning, including various loss functions, optimization algorithms like SGD, Adam, and RMSProp, and the underlying mathematics of gradient-based learning.
- Adam: A Method for Stochastic Optimization, Diederik P. Kingma and Jimmy Ba, 2015 3rd International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1412.6980 - The original paper introducing the Adam optimizer, which is widely used in deep learning for its adaptive learning rate capabilities.
- Flux.jl Documentation: Losses, Flux.jl Contributors, 2025 - Official documentation for loss functions available in Flux.jl, including
mse,binarycrossentropy, andcrossentropy, with usage examples. - Flux.jl Documentation: Optimizers, Flux.jl Contributors, 2023 - Official documentation for optimizers provided by Flux.jl, detailing their functionality and usage within the framework, including SGD, Adam, and RMSProp.
- Lecture 6a: RmsProp, Divide the gradient by a running average of its recent magnitude, Geoffrey Hinton, 2012 Coursera Lecture Notes (University of Toronto) - The original lecture notes introducing the RMSProp optimization algorithm, which is an adaptive learning rate method.