Course Name: Machine learning and data mining
Majors involved: Data Science and Big Data Technology
Course introduction：
Machine learning is the science of getting computers to act without explicitly programming. In the past decades, machine learning has given us self-driving cars, practical speech recognition, effective web search, and wide improved application in the many fields. Machine learning is so popular today that you probably use it dozens of times a day without knowing it. Many people also think it is the best way to make progress towards AI (artificial intelligence). In this course, you will learn about the most effective machine learning techniques, and also gain practice implementing them and getting them to work for you. More importantly, you'll learn about not only the theory of machine learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems, which we always called data mining.
This course provides a broad introduction to machine learning, data mining. Topics include: (i) supervised learning (which including parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems). (iii) Best practices in machine learning. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to do data mining including text understanding, computer vision, database mining, and other areas.
