Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Description

Introduction to Machine Learning with Probabilistic Models and Inference

Machine learning is essential for analyzing the vast amounts of data generated in today’s digital world. This book offers a comprehensive, self-contained introduction to machine learning, utilizing probabilistic models and inference as a unifying approach. It provides a balance between foundational material and recent advancements in the field, making it suitable for both upper-level undergraduates and beginning graduate students.

Key Features:

Unified Probabilistic Approach: Emphasizes a probabilistic model-based perspective for detecting patterns and making predictions from data.

Comprehensive Coverage: Includes essential topics such as probability, optimization, and linear algebra.

Advanced Topics: Covers recent developments like conditional random fields, L1 regularization, and deep learning.

Accessible Style: Written in an informal and approachable tone, with pseudo-code for key algorithms.

Illustrations & Examples: Contains numerous color images and worked examples across diverse application domains like biology, text processing, computer vision, and robotics.

Model-Based Approach: Focuses on using graphical models to describe algorithms and data succinctly.

Practical Tools: The book provides access to the PMTK (probabilistic modeling toolkit) MATLAB software, enabling readers to implement the models discussed.

Dive into this comprehensive resource to gain a solid understanding of machine learning and its real-world applications, and begin implementing the techniques with the accompanying MATLAB toolkit!