Data Driven Model Learning

The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail.

Becoming a Business Analytics Practitioner

Machine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, Kaggle

AI for Business : Leverage AI to Set SMART Goals, Make Strategic Decisions, and Drive Business Success

This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory.

Become a Data Driven Investor. Make Profitable, Robust, Statistically-Backed Investment Decisions | Quantitative Finance

Become a Data Driven Investor. Rigorously Test & Statistically Validate Investments From Scratch | Quantitative Finance

Become a Data Driven Investor. Rigorously Test & Statistically Validate Investments From Scratch | Quantitative Finance

Become a Data Driven Investor. Rigorously Test & Statistically Validate Investments From Scratch | Quantitative Finance