Supervised Learning in Biological Applications

Key Features:
- Understand the principles behind gradient boosting algorithms
- Explore popular algorithms such as XGBoost, LightGBM, CatBoost, and AdaBoost
- Learn how to apply gradient boosting with decision trees, linear discriminant analysis, and quadratic discriminant analysis
- Dive into advanced topics like softmax function, entropy and information gain, maximum likelihood estimation, and Bayesian inference
- Gain hands-on experience with optimization techniques such as stochastic gradient descent, Adam optimizer, and ridge, lasso, and elastic net regressions
- Master the concepts of kernel methods, radial basis function networks, Fourier and wavelet transforms, and Monte Carlo methods
- Discover the power of genetic algorithms, ant colony optimization, primal-dual methods, latent variable models, and reinforcement learning
Book Description:
Supervised Learning in Biological Applications is a comprehensive guide that brings together various supervised learning techniques with a focus on their applications in the field of biology. Whether you are a biologist, researcher, or data scientist, this book will equip you with the necessary knowledge and skills to effectively apply these algorithms to solve biological problems. Each chapter presents a different algorithm or technique, including detailed explanations, Python code examples, and practical applications.
What You Will Learn:
- Understand the principles and concepts behind gradient boosting algorithms
- Implement popular gradient boosting algorithms like XGBoost, LightGBM, and CatBoost in Python
- Apply gradient boosting with decision trees and explore its equations and model derivation
- Perform linear and quadratic discriminant analysis for classification problems
- Use softmax function for multi-class classification and input to neural networks
- Measure information gain and apply it to improve model decisions
- Implement optimization techniques such as stochastic gradient descent and Adam optimizer
- Apply ridge, lasso, and elastic net regressions for regularization and bias-variance tradeoff in linear regressions
- Explore kernel methods, radial basis function networks, Fourier and wavelet transforms
- Understand Monte Carlo methods, simulated annealing, genetic algorithms, ant colony optimization, and primal-dual methods
- Explore latent variable models, including factor analysis and independent component analysis
- Discover the principles of reinforcement learning and implement Q-learning and policy gradient algorithms
Who This Book Is For:
This book is for biologists, researchers, and data scientists interested in applying supervised learning algorithms in biological applications. You should have basic knowledge of Python programming and a background in biology or related fields. The Python code provided in each chapter will help you implement and experiment with the algorithms discussed in the book.



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