The course will start from the very beginning of the ML basis. First, the basic concepts such as liner algebra; probability and information theory, and numerical methods will be introduced. Next machine learning overview, inductive learning, and representation learning will be introduced. Basic deep learning processes are designed as artificial neural network; Bayesian Networks and learning; Deep learning and deep neural networks; convolution neural network. Throughout the course, practical methodology of using tools such as Tensorflow, Keras or Scikit-learn etc. will be emphasized.
Undestand the fundamentals of machine learning, including basic learning techniques through big dataset, and learning process flows
Use machine lerning tools (e.g., Keras, Scikit-learn and Tensorflow etc) on datasets.
Design basic learning approaches of Bayesian networks, inductive learning and representation learning etc with the tools
Design basic artificial neural networks, feedforward neural network; BP algorithm and deep models such as convolution neural networks with the tools
Introduction to Data Science Programming
Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, An MIT Press; 2016.
Remarks: This course will also be offered to the senior UG students (year-4 students) as free elective.