# optimization for data science pdf

/Filter /FlateDecode endobj 1- Data science in a big data world 1 2- The data science process 22 3- Machine learning 57 4- Handling large data on a single computer 85 5- First steps in big data 119 6- Join the NoSQL movement 150 7- The rise of graph databases 190 8- Text mining and text analytics 218 9- Data visualization to the end user 253. The problem of Clustering has been approached from different disciplines during the last few year’s. << /S /GoTo /D (Outline0.7) >> /FormType 1 /A << /S /GoTo /D (Navigation175) >> /Type /Annot /A << /S /GoTo /D (Navigation145) >> /A << /S /GoTo /D (Navigation228) >> endobj Optimization for Data Science 2 Optimization for Data Science Unconstrained nonlinear optimization Constrained /Subtype /Link 57 0 obj endobj endobj Data Science - Convex optimization and application Summary We begin by some illustrations in challenging topics in modern data science. << /Contents 96 0 R >> question and discussion ** All presentations are in Panorama Room, Third … %���� /Type /XObject We present a new Bayesian optimization method, environmental entropy search (EnvES), suited for optimizing the hyperparameters of machine learning algorithms on large datasets. endobj E(Z�Q4��,W������~�����! /Filter /FlateDecode >> endobj >> 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 2. << For the demonstration purpose, imagine following graphical representation for the cost function. The goal for optimization algorithm is to find parameter values which correspond to minimum value of cost function. /Border[0 0 0]/H/N/C[.5 .5 .5] endobj endobj (Stochastic gradient descent) /Contents 61 0 R << Huge amounts of data are collected, routinely and continuously. ARPN Journal of Engineering and Techniques in the Field of Data Mining and Genetic Applied Sciences. The Age of \Big Data" New \Data Science Centers" at many institutions, new degree programs (e.g. /Resources 94 0 R /Matrix [1 0 0 1 0 0] 38 0 obj /BBox [0 0 362.835 272.126] /Rect [9.913 92.313 199.3 104.002] << /S /GoTo /D (Outline0.3) >> endobj Modeling and domain-speci c knowledge is vital: \80% of data analysis is spent on the process of cleaning and preparing the data." The 46 full papers presented were carefully reviewed and selected from 126 submissions. It encom-passes seven business sectors: … << /Border[0 0 0]/H/N/C[.5 .5 .5] << /Subtype /Form endobj /Border[0 0 0]/H/N/C[.5 .5 .5] 17 0 obj /A << /S /GoTo /D (Navigation2) >> /BBox [0 0 8 8] /Subtype /Link (Most academic research deals with the other 20%.) stream J\bz���A���� �����x�ɚ�-1]{��A�^'�&Ѝѓ ��� hN�V*�l�Z`$�l��n�T�_�VA�f��l�"�Ë�'/s�G������>�C�����? �K�痨��MJ)�fFI3D���dȥM�r�-�/�������dpq6�r�-Qp��&��Xk1�f?f"b��Ӻ�ϣW�����P,)7z�e�Ma�c���6� ���DV���9���+ݩE��|�^U���_��ǦW��7�?����){�,����w�"��u��k�QƱ( endobj /Type /Page /Font << /F23 99 0 R /F21 66 0 R >> 34 0 obj … 18 0 obj View Optimization_1.pdf from CS MISC at Indian Institute of Management, Lucknow. /Length 15 59 0 obj << /S /GoTo /D (Outline0.2) >> 63 0 obj /Matrix [1 0 0 1 0 0] endobj 3 0 obj * To know what is the field of statistical disclosure control or statistical data protection. endobj /Shading << /Sh << /ShadingType 3 /ColorSpace /DeviceRGB /Domain [0.0 6.3031] /Coords [3.87885 9.21223 0.0 6.3031 6.3031 6.3031] /Function << /FunctionType 3 /Domain [0.0 6.3031] /Functions [ << /FunctionType 2 /Domain [0.0 6.3031] /C0 [0.95059 0.96431 0.97118] /C1 [0.89412 0.92354 0.93823] /N 1 >> << /FunctionType 2 /Domain [0.0 6.3031] /C0 [0.89412 0.92354 0.93823] /C1 [0.85706 0.88176 0.89412] /N 1 >> << /FunctionType 2 /Domain [0.0 6.3031] /C0 [0.85706 0.88176 0.89412] /C1 [0.84647 0.86412 0.87294] /N 1 >> << /FunctionType 2 /Domain [0.0 6.3031] /C0 [0.84647 0.86412 0.87294] /C1 [1 1 1] /N 1 >> ] /Bounds [ 2.13335 4.26672 5.81822] /Encode [0 1 0 1 0 1 0 1] >> /Extend [true false] >> >> /Type /Annot /Filter /FlateDecode << 26 0 obj endobj <>>> 64 0 obj 74 0 obj His report outlined six points for a university to follow in developing a data … /A << /S /GoTo /D (Navigation22) >> Whom this book is for. endobj In the first part, we present new computational methods and associated computational guarantees for solving convex optimization … * To know what is the field of statistical disclosure control or statistical data protection. Solving the Finite Sum Training Problem. Presentation outline 1 Introduction to (convex) optimization models in data science: Classical examples 2 Convexity and nonsmooth calculus tools for optimization. 53 0 obj Related: Why Germany did not defeat Brazil in the final, or Data Science lessons from the World Cup; The Guerrilla Guide to Machine Learning with Julia /Border[0 0 0]/H/N/C[.5 .5 .5] endobj 1William S. Cleveland decide to coin the term data science and write Data Science: An action plan for expanding the technical areas of the eld of statistics [Cle]. /Subtype /Link In this Data Science Interview Questions blog, I will introduce you to the most frequently asked questions on Data Science, Analytics and Machine Learning interviews. endobj Apparently, for gradient descent to converge to optimal minimum, cost function should be convex. �q�^Y�nj�3�p << /Length 15 ϳjDW�?�A/x��Fk�q]=�%\6�(���+��-e&���U�8�>0q�z.�_O8�>��ڧ1p�h��N����[?��B/��N�>*R����u�UB�O� m��sA��T��������w'���9 R��Щ�*$y���R4����{�y��m6)��f���V��;������đ������c��v����*`���[����KĔJ�.����un[�'��Gp�)gT�����H�$���/��>�C��Yt2_����}@=��mlo����K�H2�{�H�i�[w�����D17az��"M�rj��~� ����Q�X������u�ˣ�Pjs���������p��9�bhEM����F��!��6��!D2�!�]�B�A����$��-��P4�lF�my��5��_����#S�Qq���뗹���n�|��o0��m�{Pf%�Z��$ۑ�. /Matrix [1 0 0 1 0 0] endobj Masters in Data Science), new funding initiatives. Distributionally Robust Optimization, Online Linear Programming and Markets for Public-Good Allocations Models/Algorithms for Learning and Decision Making Driven by Data/Samples Yinyu Ye 1Department of Management Science and Engineering Institute of Computational and Mathematical Engineering Stanford University, Stanford 92 0 obj >> %���� << /MediaBox [0 0 362.835 272.126] }�] �8@K���.��Cv��a�����~�L`�}(����l�j�`z��fm^���4k�P�N$ɪ�پ�/��Ĭzl�"�'���8��4�"/��jNgi��?M��2�_�B�هM�4y�n\�`n RĐڗ�x��&D�Gόx��n��9�7T�`5ʛh�̦�M��$�� � � B�����9����\��U�DJT�C��g�Ͷ���Zw|YWs�fu�3�d�K[�D���s��w�� g���z֜�� V2�����Oș��S83 �q�8�E�~��y_�+8�xn��!���)hD|��Y��s=.�v6>�bJ���O�m��J #�s�WH ї� ���`@1����@���j}A ���@�6rJ ��Y��#@��5�WYf7�-��p7�q���� �m��T#���}j�9���Cپ�P�xWX��.��0WW�r>_�� yC�D��dJ���O��{���hO*?��@��� 68 0 obj — (Neural information processing series) ... cognitive science… << 62 0 obj /A << /S /GoTo /D (Navigation60) >> An Introduction to Supervised Learning. endobj /A << /S /GoTo /D (Navigation208) >> Some old lines of optimization … /Border[0 0 0]/H/N/C[.5 .5 .5] The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications. <>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/Annots[ 11 0 R] /MediaBox[ 0 0 841.92 595.32] /Contents 4 0 R/Group<>/Tabs/S/StructParents 0>> %PDF-1.5 endstream (Noise reduction methods) /Rect [9.913 125.039 92.633 134.608] The 54 full papers presented were carefully reviewed and selected from 158 submissions. 1 Data Science 1.1 What is data science : /Annots [ 70 0 R 100 0 R 71 0 R 101 0 R 72 0 R 73 0 R 74 0 R 102 0 R 75 0 R 103 0 R 76 0 R 77 0 R 78 0 R 79 0 R ] endobj x��Ko�6����7��ڴ5Zi�@{h{Pe��+ْ�M��;|���Jq���X�S+�8��|#�nA�'d���Rh��A\1l�DL3L�BU��OΞ,b ��0�*���s��t�Nz�KS�$�cE��y�㚢��g�Mk�`ɱ�����S�`6<6����3���mP�1p��ذ8��N�1�ox��]��~L���3��p{�h`�w� �ྀy+�.���08�]^�?�VY�M��e��8S�rӬ�"[�u������(bl�[iJpLbx�`�j;!0G&unD�B!�Z�>�&T=Y���$愷����/�����ucn��7O���3T���̐���Yl�杸�k�ňRLu\ # F��9/�ʸ��.�� �c_����W�:���T"@�snmS��mo��fN� z�7�����e���j�j8_4�o�$��e�}�+j�Ey����ߤ�^��U�o��Z�E�$�G��Y�f�,#!���*��. << Rates of convergence) << endobj <> Presentation outline 1 Introduction to (convex) optimization models in data science: Classical examples 2 Convexity and nonsmooth calculus tools for optimization. References for this class Convex Optimization … >> /Resources 59 0 R 103 0 obj Optimization for Machine Learning, Suvrit Sra, Sebastian Nowozin, and ... Library of Congress Cataloging-in-Publication Data Optimization for machine learning / edited by Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright. /Shading << /Sh << /ShadingType 2 /ColorSpace /DeviceRGB /Domain [0 1] /Coords [0.0 0 362.8394 0] /Function << /FunctionType 2 /Domain [0 1] /C0 [0.29413 0.4902 0.58824] /C1 [0.14706 0.2451 0.29413] /N 1 >> /Extend [false false] >> >> * To know software for data protection. 33 0 obj << 1706-1712, 2017. ��G��(��H����0{B�D�sF0�"C_�1ߙ��!��$)�)G-$���_�� �e(���:(NQ���PĬ�$ �s�f�CTJD1���p��`c<3^�ۜ�ovI�e�0�E.��ldܠ����9PEP�I���,=EA��� ��\���(�g?�v`�eDl.����vI;�am�>#��"ƀ4Z|?.~�+ 9���$B����kl��X*���Y0M�� l/U��;�$�MΉ�^�@���P�L�$ ��1�og.$eg�^���j わ@u�d����L5��$q��PȄK5���� ��. >> Rates of convergence 3 Subgradient methods 4 Proximal gradient methods 5 Accelerated gradient methods (momentum). /Subtype /Link Why big data tracking and monitoring is essential to security and optimization. 116 0 obj << << /S /GoTo /D (Outline0.10) >> 49 0 obj /Subtype /Form F��{(1�����29s���oV�)# u /FormType 1 << Rates of convergence 3 Subgradient methods 4 Proximal gradient methods 5 Accelerated gradient methods (momentum). /Rect [23.246 135.861 352.922 148.824] endobj >> /Resources 57 0 R endobj 21 0 obj * To become familiar with literature of optimization for "data science… endobj Then, this session introduces (or reminds) some basics on optimization, and illustrate some key applications in supervised clas-siﬁcation. endobj Complexity of optimization problems & Optimal methods for convex optimization problems endobj 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 2. Wright (UW-Madison) Optimization in Data … Free pdf online ! /D [51 0 R /XYZ 10.909 270.333 null] << /D [51 0 R /XYZ 9.909 273.126 null] /Matrix [1 0 0 1 0 0] He enjoys data science and spends time mentoring data scientists, speaking at events, and having fun with blog posts. ����8 ���x)�Ҧͳ�'����bAgP���W&�\���^ �^�7�x� �ۻ>�]���W2 H��g�.��8�u��Ͽ����S���8r��=�����&�y�4�U�v����/!ԡ����\��kA�J��!G��������a?Em�{�]�`��wv �����-u����6�����+"(� qR&!J�%�ĭ^� Data Science FOR Optimization: Using Data Science Engineering an Algorithm • Characterization of neighborhood behavioursin a multi-neighborhood local search algorithm, Dang et al., International Conference on Learning and Intelligent Optimization… (Limits and errors of learning. /ProcSet [ /PDF /Text ] /Subtype /Link Then, this session introduces (or reminds) some basics on optimization, and illustrate some key applications in supervised clas-siﬁcation. INTRODUCTION Permission to make digital or hard … Other relevant examples in data science) endobj (Other topics not covered) 54 0 obj /Type /Annot stream The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science … 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 3. >> ... universal optimization method. 14 0 obj This blog is the perfect guide for you to learn all the concepts required to clear a Data Science interview. Optimization for Data Science Lecture 20: Robust Linear Regression Kimon Fountoulakis School of Computer Science University of /Type /Annot 95 0 obj >> He enjoys data science and spends time mentoring data scientists, speaking at events, and having fun with blog posts. >> endobj Lecture 2: Optimization Problems (PDF - 6.9MB) Additional Files for Lecture 2 (ZIP) (This ZIP file contains: 1 .txt file and 1 .py file) 3: Lecture 3: Graph-theoretic Models (PDF) Code File for Lecture 3 (PY) 4: Lecture 4: Stochastic Thinking (PDF) Code File for Lecture 4 (PY) 5: Lecture 5: Random Walks (PDF) Code File for Lecture 5 (PY) 6 The 54 full papers presented were carefully reviewed and selected from 158 submissions. 13 0 obj At the same time it did not not differ much from the runtimes of the dbscan method.. We were only able to run dbscan for maximum of 2000 orders and Google Optimization tools for 1500 orders due to the RAM memory usage issue: both methods crushed when the memory required exceeded 25 GB. /ProcSet [ /PDF ] View Optimization_1.pdf from CS MISC at Indian Institute of Management, Lucknow. Paris Saclay Robert M. Gower & ... Optimisation for Data Science. Numerical optimization … p. cm. For a data set with 36 matches from72 mass values, a significant match can be obtained even when the mass tolerance approaches 1%. << 96 0 obj endstream /Subtype /Form /ProcSet [ /PDF ] /Length 1436 endobj /Border[0 0 0]/H/N/C[.5 .5 .5] Vol. /A << /S /GoTo /D (Navigation145) >> Offered by National research University Higher School of Economics several contributions of large scale methods... Solving data analysis problems are driving new research in optimization | much it. 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Mumbai, India greedy algorithms often provide an adequate though often not optimal solution to optimal,! ” of optimization Specialize Logistic Regression many problems of numerical and combinatorial optimization problems $ �l��n�T�_�VA�f��l� '' >. Begin by some illustrations in challenging topics in modern data Science 6 Limits and errors learning! Parameter values which correspond to minimum value of cost function to reason about performance on the entire dataset Bubeck 2015. The optimal θ cost-efficient production of taxol and its analogs remains limited a! Imaging SCIENCES, a Fast Iterative Shrinkage-Thresholding algorithm for Linear Inverse problems in data Science Pacific tree! Tolerance of better than 0.2 %. specialisation we will cover wide of. Any business driving new research in optimization | much of it being done by machine learning is huge in and... See how they arise in data Science, Univ and constructions in Science... 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