All About PCA:
Theory, Algorithm, applications

In this age of big data, many algorithms are restricted by large dimensional data, commonly known as the “Curse of Dimensionality”. Dimensionality reduction is a class of methods that can alleviate this problem. In this workshop, we introduce a dimensionality reduction algorithm in-depth, Principal Component Analysis (PCA), covering the statistics and linear algebra prerequisites required, the PCA algorithm and what it computes, and situate the algorithm in a diverse range of applications: Machine Learning, Biology and Finance. The workshop is conducted by the Workshops team’s most diverse people: Ang Yi Zhe (Data Science and Analytics), Ang Ming Liang (Computational Biology and Mathematics) and Zheng Peng (Quantitative Finance).

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