This course introduces commonly used models and algorithms in data science fields. Both supervised and unsupervised machine learning algorithms will be discussed. Specific topics will be selected from supervised learning (probabilistic and linear classification, neural networks, tree-based models), unsupervised learning (clustering and feature extraction) and semi-supervised learning algorithms. This course will introduce both theories and applications. 

• Introduction to statistical machine learning 
• Featuring engineering
• Probabilistics and statistical models
• Traditional machine learning and extensions
• Clustering algorithms 
• Mixture models
• Statistical machine learning for financial risk modeling

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