Machine learning is a sub-field of Artificial Intelligence that is concerned with the design and analysis of algorithms that "learn" and improve with experience, While the broad aim behind research in this area is to build systems that can simulate or even improve on certain aspects of human intelligence, algorithms developed in this area have become very useful in analyzing and predicting the behavior of complex systems.
Machine learning algorithms have been used to guide diagnostic systems in medicine, recommend interesting products to customers in e-commerce, play games at human championship levels, and solve many other very complex problems.
This course is an introduction to algorithms for machine learning and their implementation in the context of big data. This includes different learning scenarios, the different algorithms that have been developed for these scenarios, and learn about how to implement these algorithms and evaluate their behavior in practice. The course also includes active discussions on dealing with noise, missing values, scalability properties and talks about tools and libraries available for these methods.
At the end of the course, you should be able to:
Machine learning algorithms have been used to guide diagnostic systems in medicine, recommend interesting products to customers in e-commerce, play games at human championship levels, and solve many other very complex problems.
This course is an introduction to algorithms for machine learning and their implementation in the context of big data. This includes different learning scenarios, the different algorithms that have been developed for these scenarios, and learn about how to implement these algorithms and evaluate their behavior in practice. The course also includes active discussions on dealing with noise, missing values, scalability properties and talks about tools and libraries available for these methods.
At the end of the course, you should be able to:
- Understand when to use machine learning algorithms; --Understand, represent and formulate the learning problem;
- Apply the appropriate algorithm(s) or tools, with an understanding of the tradeoffs involved including scalability and robustness;
- Correctly evaluate the behavior of the algorithm when solving the problem.
