LARGE-SCALE DATA ANALYSIS AND MINING IN MEDICINE AND BIOLOGY

Description.

Large-scale data analysis and mining has attracted the interest of many researchers in recent years, having emerged as one of the key intersections of computer science. The purpose of data mining is to find correlations and extract patterns that may exist in large-scale data so that they can efficiently solve a problem. In this course we will deal with the extraction of patterns from large-scale data from medicine and biology, studying modern algorithms and techniques. We will talk about clustering methods also known as unsupervised learning methods and dimension reduction methods for multidimensional data. In addition, methods of data visualization will be studied in order to draw useful conclusions. The methods and algorithms that will be studied include, apart from the well-known k-means algorithm, hierarchical algorithms and density algorithms for the purpose of data clustering. Modern data dimension reduction and visualization algorithms (s-SNE, probabilistic PCA, etc.).

Bibliography:
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