Statistical Learning for Engineering Part 2

This course covers practical algorithms and the theory for machine learning from a variety of perspectives. Topics include supervised learning (generative, discriminative learning, parametric, non-parametric learning, deep neural networks, support vector Machines), unsupervised learning (clustering, dimensionality reduction, kernel methods). The course will also discuss recent applications of machine learning, such as computer vision, data mining, natural language processing, speech recognition and robotics. Students will learn the implementation of selected machine learning algorithms via python and PyTorch

• Understand deep learning algorithms and advanced models
• Apply transfer learning and decision tree techniques
• Implement neural networks with Python and PyTorch

• Basic familiarity with machine learning terminology
• Willingness to practice through applied exercises or case studies