Over the past two decades Machine Learning has become one of the mainstays of information technology and with that, a rather central, albeit usually hidden, part of our life. With the ever increasing amounts of data becoming available there is good reason to believe that smart data analysis will become even more pervasive as a necessary ingredient for technological progress.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behaviour, optimize robot behaviour so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program.
Machine learning is one of the top value-producing digital innovation trends, ranking fourth in our Top 10 Technology Trends assessment. Machine learning, which includes predictive analytics, covers cognitive systems that go beyond Big Data Analytics. It give enterprises the capability to not only discover patterns and trends from increasingly large and diverse datasets but also enables them to automate analysis that has traditionally been done by humans, to learn from business-related interactions and deliver evidence based responses. It also provides confidence levels in the likely success of recommended actions. It gives enterprises the capability to deliver new differentiated/ personalized products and services, as well as increasing the effectiveness and/or lowering the cost of existing products and services. Machine learning initiatives should be considered, not only as strategic initiatives, but for their possible effect on other business strategies. However, deployment can carry business risk, so investment decisions should be approached with care.
- Suitable for individuals who have knowledge in algebra and calculus.
- Linear algebra.
- Probability theory.
- Calculus of variations.
- Graph theory.
- Optimization methods (Lagrange multipliers).