Machine Learning with Julia

Class 1: Introduction to Machine Learning Concepts

Objective: Understand the basic concepts and types of machine learning.

Agenda:

1. Introduction to Machine Learning (10 minutes)
   • Definition and importance of machine learning
   • Applications in various fields
   • Difference between AI, machine learning, and deep learning
2. Types of Machine Learning (20 minutes)
   • Supervised learning
   • Unsupervised learning
   • Semi-supervised learning
   • Reinforcement learning
3. Machine Learning Workflow (20 minutes)
   • Data collection and preprocessing
   • Feature engineering
   • Model training and evaluation
   • Model deployment
4. Interactive Discussion (5 minutes)
   • Q&A session to address initial questions
   • Summary of key points and introduction to the next class

Class 2: Supervised and Unsupervised Learning

Objective: Learn about the principles of supervised and unsupervised learning and their applications.

Agenda:

1. Recap of Previous Class (5 minutes)
   • Quick review of machine learning concepts
2. Supervised Learning (15 minutes)
   • Types of supervised learning (regression, classification)
   • Common algorithms (Linear Regression, Decision Trees, SVM, etc.)
   • Practical examples and applications
   • Packages: MLJ.jl, ScikitLearn.jl
3. Unsupervised Learning (15 minutes)
   • Types of unsupervised learning (clustering, dimensionality reduction)
   • Common algorithms (K-Means, PCA, etc.)
   • Practical examples and applications
   • Packages: Clustering.jl, MultivariateStats.jl
4. Interactive Coding Session (20 minutes)
   • Practice exercises on implementing supervised and unsupervised learning algorithms in Julia
   • Packages: MLJ.jl, ScikitLearn.jl, Clustering.jl, MultivariateStats.jl
5. Q&A and Wrap-up (5 minutes)
   • Open floor for questions
   • Summary of key points
   • Preview of the next class

Class 3: Implementing Machine Learning Algorithms in Julia

Objective: Implement and understand the working of various machine learning algorithms using Julia.

Agenda:

1. Recap of Previous Class (5 minutes)
   • Quick review of supervised and unsupervised learning
2. Implementing Regression Algorithms (15 minutes)
   • Linear Regression, Ridge Regression, Lasso Regression
   • Practical coding examples
   • Packages: MLJ.jl, GLM.jl
3. Implementing Classification Algorithms (15 minutes)
   • Logistic Regression, Decision Trees, Support Vector Machines
   • Practical coding examples
   • Packages: MLJ.jl, DecisionTree.jl, LIBSVM.jl
4. Implementing Clustering Algorithms (15 minutes)
   • K-Means, Hierarchical Clustering
   • Practical coding examples
   • Packages: Clustering.jl
5. Interactive Coding Session (10 minutes)
   • Practice exercises on implementing machine learning algorithms
   • Packages: MLJ.jl, GLM.jl, DecisionTree.jl, LIBSVM.jl, Clustering.jl
6. Q&A and Wrap-up (5 minutes)
   • Open floor for questions
   • Summary of key points
   • Preview of the next class

Class 4: Cross-Validation and Model Selection

Objective: Understand the concepts of cross-validation and model selection techniques in machine learning.

Agenda:

1. Recap of Previous Class (5 minutes)
   • Quick review of implementing machine learning algorithms
2. Introduction to Model Evaluation (10 minutes)
   • Importance of model evaluation
   • Metrics for regression and classification
   • Packages: MLMetrics.jl, ROCAnalysis.jl
3. Cross-Validation Techniques (20 minutes)
   • K-Fold Cross-Validation, Leave-One-Out Cross-Validation
   • Implementing cross-validation in Julia
   • Packages: MLJ.jl, CrossValidation.jl
4. Model Selection and Hyperparameter Tuning (15 minutes)
   • Grid Search, Random Search, Bayesian Optimization
   • Practical coding examples
   • Packages: MLJ.jl, Hyperopt.jl
5. Interactive Coding Session (10 minutes)
   • Practice exercises on cross-validation and model selection
   • Packages: MLJ.jl, CrossValidation.jl, Hyperopt.jl
6. Q&A and Wrap-up (5 minutes)
   • Open floor for questions
   • Summary of key points
   • Overview of Week 9 and additional resources for further learning