Location:Training / Machine Learning/Algorithm / Machine Learning Andrew Ng / Map Reduce and Data Parallelism

This course will provide a broad introduction to machine learning, data mining and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Excellent cases of machine learning (bias/variance theory; innovation process of machine learning and artificial intelligence) course will focus on case studies and applications to learn how to apply learning algorithms to intelligent robots (perception, control), text understanding ( Web search, anti-spam), computer vision, medical informatics, audio, data mining and other fields.