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PREFACE:

Machine learning is rapidly becoming a prominent area within computer science, with applications spanning numerous fields.

This textbook aims to introduce machine learning and its algorithmic frameworks in a structured manner.

 It offers a comprehensive theoretical overview of the core concepts that underpin machine learning and the mathematical formulas that convert these concepts into practical algorithms. 

After presenting the basics, the book delves into a variety of essential topics not typically covered in other textbooks.

 These topics include the computational complexity of learning, concepts of convexity and stability, key algorithmic methods such as stochastic gradient descent, neural networks, and structured output learning, as well as new theoretical ideas like the PAC-Bayes approach and compression-based bounds.

Intended for advanced undergraduates or beginning graduate students, the text makes the principles and algorithms of machine learning accessible to students and non-experts in fields such as statistics, computer science, mathematics, and engineering.