【2022新书】机器学习中的统计建模:概念和应用，398页pdf

《机器学习中的统计建模:概念和应用》介****绍了统计、探索性数据分析和机器学习的基本概念和作用。本文将讨论机器学习的各个方面以及统计的基础知识。通过简单的例子和图形表示来介绍概念，以便更好地理解技术。这本书采取了一个整体的方法-把关键概念与机器学习的多学科应用的深入论述放在一起。讨论了新的案例研究和研究问题陈述，这将帮助研究人员在他们的应用领域基于统计和机器学习的概念。机器学习中的统计建模:概念和应用将帮助统计学家、机器学习从业者和程序员解决各种任务，如分类、回归、聚类、预测、推荐等。

https://www.elsevier.com/books/statistical-modeling-in-machine-learning/goswami/978-0-323-91776-6

通过实际问题、应用和教程的帮助，提供了应用于机器学习的最新统计概念的全面概述 * 介绍了从基本原理到先进技术的逐步方法 * 包括成功和不成功的机器学习应用的案例研究，以理解其实现中的挑战，以及工作的例子

1. Introduction to Statistical Modelling in Machine Learning - A Case Study
2. A Technique of Data Collection- Web Scraping with Python
3. Analysis of Covid-19 using Machine Learning Techniques
4. Discriminative Dictionary Learning based on Statistical Methods
5. Artificial Intelligence based Uncertainty Quantification technique for External flow CFD simulations
6. Music Genres Classification
7. Classification Model of Machine Learning for Medical Data Analysis
8. Regression Models for Machine learning
9. Model Selection and Regularization
10. Data Clustering using Unsupervised Machine Learning
11. Emotion-based classification through fuzzy entropy enhanced FCM clustering
12. Fundamental Optimization Methods for Machine Learning
13. Stochastic Optimization of Industrial Grinding Operation through Data-Driven Robust Optimization
14. Dimensionality Reduction using PCAs in Feature Partitioning Framework
15. Impact of Mid-Day Meal Scheme in Primary Schools in India using Exploratory Data Analysis and Data Visualisation
16. Nonlinear System Identification of Environmental pollutants using Recurrent Neural Networks and Global Sensitivity Analysis
17. Comparative Study of Automated Deep Learning Techniques for Wind Time Series Forecasting