In the last decade, our information society has mutated into a data society, where the volume of worldwide data doubles every 1.5 years. How to make sense of such tremendous volume of data? Developing effective techniques to extract meaningful information from large-scale and high-dimensional dataset has become essential for the success of business, government and science.
The goal of this course is to provide a broad introduction to effective algorithms in data science and network analysis. A major effort will be given to show that existing data analysis techniques can be defined and enhanced on graphs. Graphs encode complex structures like cerebral connection, stock exchange, and social network. Strong mathematical tools have been developed based on linear and non-linear graph spectral harmonic analysis to advance the standard data analysis algorithms. Main topics of the course are networks, unsupervised and supervised learning, recommendation, visualization, sparse representation, multi-resolution analysis, neuron network, and large-scale computing.
The course is organized into two parts: lectures (2 hours) and coding exercises (1 hour). The essential objective of the exercises is to apply the theory on real-world cases. Evaluation will be conducted on a continuous basis: homeworks and coding assignments.
data science, data mining, network science, machine learning
The official course website is in Moodle (EPFL credentials required).