Dive deep into the fascinating world of machine learning with the Machine Learning Algorithms in Depth repository. This comprehensive resource is designed to strengthen your understanding of both classic and modern machine learning algorithms, encompassing both Bayesian inference and deep learning.

This project is structured into several chapters, covering key topics and practical implementations:

Chapter Overviews

Chapter 2: Markov Chain Monte Carlo (MCMC)

• Monte Carlo Estimate of Pi: An implementation that estimates Pi using Monte Carlo methods.
• Binomial Tree Model: A Monte Carlo simulation that models binomial stock prices.
• Random Walk: A self-avoiding random walk generator.
• Gibbs Sampling: Multivariate Gaussian distribution sampling using Gibbs methods.
• Metropolis-Hastings Sampling: Implementation for multivariate Gaussian mixtures.
• Importance Sampling: A method for estimating expected values of functions.

Chapter 3: Variational Inference (VI)

• Mean Field VI: Application of mean field variational inference for image denoising using Ising model.

Chapter 4: Software Implementation

• Subset Generation: A complete search algorithm for generating subsets.
• Fractional Knapsack: Implementation of a greedy algorithm for the knapsack problem.
• Binary Search: A divide-and-conquer algorithm for efficient searching.
• Binomial Coefficients: Dynamic programming approach for calculating binomial coefficients.

Chapter 5: Classification Algorithms

• Perceptron: A foundational algorithm for binary classification.
• Support Vector Machine (SVM): A powerful algorithm for large margin classification.
• Stochastic Gradient Descent Logistic Regression (SGD-LR): An efficient method for logistic regression.
• Bernoulli Naive Bayes: Implementation of the Naive Bayes classifier for binary features.
• Decision Tree (CART): A comprehensive implementation of the classification and regression trees.

Chapter 6: Regression Algorithms

• K-Nearest Neighbors Regression (KNN): A simple yet effective regression technique.
• Bayesian Linear Regression (BLR): An implementation of Bayesian methods for regression.
• Hierarchical Bayesian Regression (HBR): A sophisticated approach to regression analysis.
• Gaussian Process Regression (GPR): Utilize GPR for predictive modeling and inference.

Chapter 7: Selected Supervised Learning Algorithms

• Page Rank Algorithm: Dive into Google's foundational ranking algorithm.
• Hidden Markov Models (HMM): Utilize the EM algorithm for HMMs.
• Imbalanced Learning: Methods including Tomek Links and SMOTE for handling imbalanced datasets.
• Active Learning: Demonstrations using logistic regression.
• Bayesian Optimization: A systematic approach for optimizing functions.
• Ensemble Learning Techniques: Explore bagging, boosting, and stacking methods for improved predictions.

Chapter 8: Unsupervised Learning Algorithms

• Dirichlet Process K-Means (DP-Means): Implementation of a nonparametric clustering approach.
• EM Algorithm for Gaussian Mixture Models (EM-GMM): A technique for clustering using Gaussian mixtures.
• Principal Component Analysis (PCA): A foundational method for dimensionality reduction.
• t-SNE: A technique for visualizing high-dimensional data.

Chapter 9: Selected Unsupervised Learning Algorithms

• Latent Dirichlet Allocation (LDA): Variational inference for topic modeling.
• Kernel Density Estimator (KDE): A non-parametric way to estimate the probability density function.
• Tangent Portfolio Optimization (TPO): Approaches for optimizing portfolios.
• Inverse Covariance Estimation (ICE): Techniques for estimating covariance inverses.
• Simulated Annealing (SA): A probabilistic technique for approximating the global optimum.
• Genetic Algorithm (GA): An evolutionary algorithm for optimization.

Chapter 10: Fundamental Deep Learning Algorithms

• Multi-Layer Perceptron (MLP): A basic neural network architecture.
• LeNet: Classic convolutional neural network for digit classification in MNIST.
• ResNet: Advanced image processing using deep residual networks.
• LSTM Models: Apply LSTMs for tasks like sentiment analysis of movie reviews.
• Multi-Input Neural Net Model (MINN): Analysis of Quora question pairs.
• Neural Net Optimizers (OPT): Explore different optimizers for neural networks.

Chapter 11: Advanced Deep Learning Algorithms

• LSTM Variational Autoencoder (LSTM-VAE): Detect anomalies in time-series data.
• Mixture Density Network (MDN): A network aimed at modeling distributions.
• Transformer Models: For effective text classification tasks.
• Graph Neural Network (GNN): A framework for learning on graph data structures.

Additional Resources

This repository also features links to the Manning Early Access Preview (MEAP) book associated with this project, which serves as an excellent starting point for anyone looking to develop a strong mathematical foundation in machine learning techniques.

You can find the book at Manning Publications.

Whether you're a beginner or looking to deepen your expertise, this repository serves as an invaluable source of knowledge for understanding and implementing machine learning algorithms effectively.