Machine Learning for Engineers

This Self-Contained Introduction To Machine Learning Designed From The Start With Engineers In Mind Will Equip Students With Everything They Need To Start Applying Machine Learning Principles And Algorithms To Real-World Engineering Problems. With A Consistent Emphasis On The Connections Between Estimation Detection Information Theory And Optimization It Includes: An Accessible Overview Of The Relationships Between Machine Learning And Signal Processing Providing A Solid Foundation For Further Study; Clear Explanations Of The Differences Between State-Of-The-Art Techniques And More Classical Methods Equipping Students With All The Understanding They Need To Make Informed Technique Choices; Demonstration Of The Links Between Information-Theoretical Concepts And Their Practical Engineering Relevance; Reproducible Examples Using Matlab Enabling Hands-On Student Experimentation. Assuming Only A Basic Understanding Of Probability And Linear Algebra And Accompanied By Lecture Slides And Solutions For Instructors This Is The Ideal Introduction To Machine Learning For Engineering Students Of All Disciplines.