必修课

This course introduces basic theory, methodologies and tools for machine learning. It will cover supervised learning (support vector machine, neural network and kernel methods), unsupervised learning (ensemble, dimension reduction and deep learning), and applications of machine learning.

This course teaches the fundamental theory and techniques for data processing and data mining. The course covers data pre-processing, feature selection and extraction, pattern discovery from data, classification, clustering and outlier detection.

Through a combination of case studies, theoretical concepts and frameworks used in business applications, this course introduces the theory and practice of Web Analytics and User Experience research. It provides a theoretical prospective on what Web Analytics is and the KPIs used in Web Analytics. The second part of the course focuses on UX Research and draws heavily on applied statistics and experimental design. Other topics covered in the course are social media, network security and user’s privacy. The tutorials will use the R programming language.

This course covers basic optimization theory and algorithms for optimization problems in machine learning and data science. Basic theory covered will include convex analysis and optimality conditions for unconstrained and constrained problems. Basic optimization algorithms such as the gradient method, the proximal gradient method and its accelerated versions, the alternating direction method of multipliers, and stochastic approaches will be presented. Second-order algorithms such as Newton’s method and inexact Newton methods will also be introduced. The course will require algorithms implementation and problem solving on computers.

As the web technology and mobile use rapidly evolves, the volume of user-generated data expand exponentially. The distillation of knowledge from such a large amount of unstructured, dynamically changed data is an extremely difficult task without the help of distributed techniques. This course introduces most state-of-the-art Big data analytical concepts, techniques and tools. By taking this course, students will gain hands-on technical experiences on solving Big Data problems using distributed algorithms and tools widely adopted in industry. The topics include basic concepts about Big Data, installation and configuration of Hadoop and Spark under a multi-node environment, distributed algorithms (recommender systems, clustering, classification, topic models, and network analysis), web crawling and web data extraction using major application programming interfaces.

This course introduces students to the key concepts of database systems, the basics of the Structured Query Language (SQL) as well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort. The course covers concepts of database systems, basics of SQL, Data Models and Relational SQL, Many-to-Many Relationships in SQL, Databases and Visualization, introduction to NoSQL.

The objective of this course is to provide students with Fintech theory and practice. This course covers the applications of new technologies like big data, block chain, and artificial intelligence (AI) in financial services, the new forms of financial services such as peer to peer lending and crowdfunding, cryptocurrencies, and Fintech regulations. Representatives from banks, hedge funds, and insurance companies will share the recent Fintech development in their companies with students.