Data Science

The course Machine Learning for Data Science aims at providing students with
a basic understanding of the principles of machine learning algorithms and
deriving practical solutions using predictive analytics. The course demonstrates
mathematical concepts and hands-on skills required for the algorithms that are
typically used in practice. The students will be able to apply concepts and skills
to analyze data across different domains, interpret the findings, then build
learning systems and comprehend their performance. Topics include: supervised
learning (Naïve Bayes classification, Decision Tree, Random Forest, Support
Vector Machine, Deep Neural Network); unsupervised learning (K-means
clustering, Hierarchical clustering). The course will also discuss recent
applications of machine learning, such as to robotics, autonomous driving
systems, face and speech recognition, text and web data processing, etc