Implementing AI Algorithms from Scratch in C++ (Part 2)

Build a deeper understanding of artificial intelligence by implementing algorithms directly in C++. This advanced path is designed for learners with prior experience in programming and foundational machine learning concepts.

Develop neural network components such as perceptrons, activation functions, and multilayer architectures. Implement unsupervised learning techniques including k-means, mini-batch k-means, principal component analysis, and DBSCAN, then evaluate clustering results with established metrics.

Create ensemble models using bagging, random forests, AdaBoost, and stacking. By focusing on algorithmic details rather than high-level abstractions, you will examine how these models learn, combine predictions, and process data.

Syllabus

Implement perceptrons, activation functions, and multilayer neural networks in C++
Build k-means, mini-batch k-means, and DBSCAN clustering algorithms
Apply principal component analysis for dimensionality reduction
Evaluate clusters using homogeneity, completeness, and v-measure
Construct bagging, random forest, AdaBoost, and stacking models
Analyze how neural, clustering, and ensemble algorithms operate internally