Description

• Introduction to machine learning
• Data transformations & feature Selection
• Algorithms: The basic method
• Credibility: Evaluating what’s been learned
• Supervised learning (neural networks, probabilistic methods)
• Unsupervised learning
• Ensemble learning
• Natural language processing.