 
שיטות Sketching לניתוח מטריצות ומידע
Sketching Methods for Matrix and Data Analysis 
03724004  

מדעים מדויקים  

Matrix computations lie at the core of a broad range of methods in data analysis and machine learning, and certain numerical linear algebra primitives (e.g. linear leastsquares regression and principal component analysis) are widely and routinely used. Devising scalable algorithms that enable large scale computation of the aforementioned primitives is crucial for meeting the challenges of Big Data applications. Sketching has recently emerged as a powerful dimensionality reduction technique for scalingup these primitives in the presence of massive data, for an extensive class of applications. This course will give an introduction to sketching theory and practice. Applications of the techniques for data analysis and machine learning applications will also be discussed.
The course will cover the following subjects, time permitting:
Random projections, JohnsonLindenstrauss lemmas and subspace embeddings
Approximating matrix multiplication; scalar and matrix concentration inequalities
Faster least squares linear regression
Low rank matrix approximations, approximate PCA and CCA
Sketching methods for kernelbased learnin
Streaming algorithms