optimization for data science pdf

F��{(1�����29s���oV�)# u endobj Organizations adopt different databases for big data which is huge in volume and have different data models. << >> << 78 0 obj /Resources 93 0 R 14 0 obj "]wPLk�R� s�%���q_�����B�twqA�u{�i�K޶M"�*��j����T|�?|�-�� >> /A << /S /GoTo /D (Navigation22) >> In this presentation, we discuss recent Mixed-Integer NonLinear Programming models that enhance the interpretability of state-of-art supervised learning tools, while preserving their good learning performance. 101 0 obj 93 0 obj endobj Algorithm.” International Journal of Advanced Trends in [27] H. Pourrahmani, M. Siavashi and M. Moghimi, “Design Computer Science and Engineering (IJATCSE). 73 0 obj /Length 1124 41 0 obj Data Science - Convex optimization and application Summary We begin by some illustrations in challenging topics in modern data science. >> (Noise reduction methods) Convex optimization and Big Data applications October, 2016 Other relevant examples in data science) J\bz���A���� �����x�ɚ�-1]–{��A�^'�&Ѝѓ ��� hN�V*�l�Z`$�l��n�T�_�VA�f��l�"�Ë�'/s�G������>�C�����? An Luong. /FormType 1 33 0 obj This blog is the perfect guide for you to learn all the concepts required to clear a Data Science interview. >> endobj /Subtype /Form endobj endstream >> View Lecture20.pdf from CS 794 at University of Waterloo. 58 0 obj /Matrix [1 0 0 1 0 0] If the data /XObject << /Fm5 68 0 R >> /Rect [23.246 155.645 148.269 168.001] <> 94 0 obj <> /Rect [23.246 211.928 352.922 224.284] We start with defining some random initial values for parameters. /Rect [9.913 125.039 92.633 134.608] 71 0 obj endobj >> /FormType 1 x��T�N�0}�������:ۉc ��r+h�>U�,7��������amL]ބ��F�Wټ�2S���>��p2�'�40� ��!H��#M�E9D0w����`p�_����;PS��M xL�&xJw��� �r�\�ώ endstream << endobj /Subtype /Link endobj endobj /Contents 96 0 R The 46 full papers presented were carefully reviewed and selected from 126 submissions. ... universal optimization method. (Proximal gradient methods) Solving the Finite Sum Training Problem. /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. << ���Gl�4qKb���E�D:ґ��>�M�="���WR()�OPCO�\"��,A�E��W��kI��"J�!�D`�ʊ��B0aR��Ϭ@��bP�س��af�`a�Bj����p�]?7�T,(�I��Ԟ���^h�4q�%��!n�w��s�w�[?����v��~O]O� �_|WH�M9��G �ucL_�D��%�ȭ�L\�qKAwBC|��^´G endobj Q܋���qP������k�2/�#O�q������� ��^���#�(��s��8�"�����/@;����ʺsY�N��V���P2�s| endobj Using the demand and trip duration data, a Mixed Integer Programming (MIP) model was developed to find the optimal driving schedule for drivers. presentation and 5 Min. * To know what is the field of statistical disclosure control or statistical data protection. 100 0 obj 37 0 obj /FormType 1 /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 ] << /S /GoTo /D (Outline0.2) >> endobj The particular requirements of data analysis problems are driving new research in optimization | much of it being done by machine learning researchers. (Introduction to \(convex\) optimization models in data science: Classical examples) /D [95 0 R /XYZ 9.909 273.126 null] ����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^� x��YKs�4��Wh�,"��$vpy�7;`a��Ll��S [Dasu and Johnson, 2003]. 18 0 obj >> Nonsmooth optimization: cutting planes, subgradient methods, successive approximation, ... Duality Numerical linear algebra Heuristics Also a LOT of domain-speci c knowledge about the problem structure and the type of solution demanded by the application. /ProcSet [ /PDF ] 52 0 obj I Consumer and citizen data… The other problem with MLE is the logistical problem of actually calculating the optimal θ. /A << /S /GoTo /D (Navigation229) >> * To know software for data protection. /Subtype /Form /Type /Annot /Subtype /Form It encom-passes seven business sectors: communications and information technology, engineering, materials, services, energy, consumer products and chemicals. How it uses data science: Instagram uses data science to target its sponsored posts, which hawk everything from trendy sneakers to dubious "free watches." endobj 57 0 obj >> >> endobj endobj /Trans << /S /R >> ϳjDW�?�A/x��Fk�q]=�%\6�(���+��-e&���U�8�>0q�z.�_O8�>��ڧ1p�h��N����[?��B/��N�>*R����u�UB�O� m��sA��T��������w'���9 R��Щ�*$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�. endobj 46 0 obj /BBox [0 0 5669.291 8] It is important to understand it to be successful in Data Science. >> 4 0 obj (Stochastic gradient descent) stream IMAGING SCIENCES, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems. x���P(�� �� Peter Nystrup 1. is a postdoctoral fellow in the Centre for Mathematical Sciences at Lund University in Lund, Sweden, and in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark in Lyngby, Denmark. Rates of convergence) 10 0 obj << It turned out that the recursive-dbscan algorithm greatly outperformed the Google Optimization Tools method. The 54 full papers presented were carefully reviewed and selected from 158 submissions. /Length 1175 Why big data tracking and monitoring is essential to security and optimization. Rates of convergence 3 Subgradient methods 4 Proximal gradient methods 5 Accelerated gradient methods (momentum). /Subtype /Link 1 Data Science 1.1 What is data science : << x���P(�� �� endobj In this thesis, we present several contributions of large scale optimization methods with the applications in data science and machine learning. Complexity of optimization problems & Optimal methods for convex optimization problems 1. 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. << 2018 Conference on Optimization and Data Science Program Schedule * Each talk includes 30 Min. >> stream /A << /S /GoTo /D (Navigation60) >> 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… >> << /S /GoTo /D [51 0 R /Fit] >> — (Neural information processing series) ... cognitive science… Then, this session introduces (or reminds) some basics on optimization, and illustrate some key applications in supervised clas-sification. >> /ProcSet [ /PDF /Text ] << /Border[0 0 0]/H/N/C[.5 .5 .5] /D [51 0 R /XYZ 9.909 273.126 null] Optimization is hard (in general) Need assumptions! 54 0 obj << >> (References) endobj << /Filter /FlateDecode 2 0 obj Table: Sample of Trip Duration Data (cleaned) used for the model Part 3: Methods. Evolutionary Computation, Optimization and Learning Algorithms for Data Science Farid Ghareh Mohammadi1, M. Hadi Amini2, and Hamid R. Arabnia1 1: Department of Computer Science, Franklin College of Arts and Sciences, University of Georgia, Athens, Georgia, 30601 2: School of Computing and Information Sciences, College of Engineering and Computing, Many algorithms have been developed in recent years for solving problems of numerical and combinatorial optimization problems. These approaches provide optimal solutions avoiding consumption of many computational resources. 55 0 obj DATA SCIENCE OPTIMIZATION COMPANY OVERVIEW Tata Group is an Indian multinational conglomerate company headquartered in Mumbai, India. Single Chapter PDF Download ... is a very general way to frame a large class of problems in data science. 82 0 obj /BBox [0 0 362.835 3.985] endobj 30 0 obj >> >> (peter.nystrup{at}matstat.lu.se) 2. Introduction to \(nonconvex\) optimization models in supervised machine learning) /Matrix [1 0 0 1 0 0] 64 0 obj /A << /S /GoTo /D (Navigation22) >> <>/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>> endobj endobj His report outlined six points for a university to follow in developing a data analyst curriculum. /Shading << /Sh << /ShadingType 3 /ColorSpace /DeviceRGB /Domain [0 1] /Coords [4.00005 4.00005 0.0 4.00005 4.00005 4.00005] /Function << /FunctionType 2 /Domain [0 1] /C0 [0.5 0.5 0.5] /C1 [1 1 1] /N 1 >> /Extend [true false] >> >> 59 0 obj * The ability to protect data using any existing technique. His report outlined six points for a university to follow in developing a data … 103 0 obj /Length 15 (Limits and errors of learning. /Rect [23.246 135.861 352.922 148.824] Even though finding an optimal solution is, in theory, exponentially hard, dynamic programming really often yields great results. /A << /S /GoTo /D (Navigation228) >> /Type /XObject /A << /S /GoTo /D (Navigation112) >> Optimization for Data Science Lecture 20: Robust Linear Regression Kimon Fountoulakis School of Computer Science University of Format: PDF, ePub, Mobi View: 1309 Get Books This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. /Border[0 0 0]/H/N/C[.5 .5 .5] 97 0 obj << Paris Saclay Robert M. Gower & ... Optimisation for Data Science. 26 0 obj >> With a smaller data set, 13 matches from 24, a significant match requires a mass tolerance of better than 0.2%. endobj EnvES executes fast algorithm runs on subsets of the data and probabilistically extrapolates their performance to reason about performance on the entire dataset. In the first part, we present new computational methods and associated computational guarantees for solving convex optimization … << /S /GoTo /D (Outline0.7) >> IBM Decision Optimization and Data Science 3 More often, however, a decision optimization application is used as an interactive decision support tool by the decision maker in a what-if iterative … /Length 15 (Convexity and nonsmooth calculus tools for optimization. %���� << << << /Rect [9.913 231.106 66.299 242.795] << 2 Optimization Algorithms for Data Analysis 33 5 Prox-Gradient Methods29 34 6 Accelerating Gradient Methods32 35 6.1 Heavy-Ball Method32 36 6.2 Conjugate Gradient33 37 6.3 Nesterov’s Accelerated … /Rect [23.246 244.049 352.922 257.011] /Rect [23.246 105.256 352.922 118.218] 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 2. 1706-1712, 2017. x���P(�� �� << >> stream endobj Stochastic gradient descent (SGD) is the simplest optimization algorithm used to find parameters which minimizes the given cost function. /Length 15 endstream /Matrix [1 0 0 1 0 0] endobj endobj Lastly, for the Ugandan Revenue Authority, they had an interest in data science … It will be of particular interest to the data science, computer science, optimization… << endobj /Parent 67 0 R 50 0 obj endobj As the data set becomes larger, high accuracy becomes less critical. /Resources 82 0 R For a data set with 36 matches from72 mass values, a significant match can be obtained even when the mass tolerance approaches 1%. /Shading << /Sh << /ShadingType 2 /ColorSpace /DeviceRGB /Domain [0.0 8.00009] /Coords [0 0.0 0 8.00009] /Function << /FunctionType 3 /Domain [0.0 8.00009] /Functions [ << /FunctionType 2 /Domain [0.0 8.00009] /C0 [1 1 1] /C1 [0.5 0.5 0.5] /N 1 >> << /FunctionType 2 /Domain [0.0 8.00009] /C0 [0.5 0.5 0.5] /C1 [0.5 0.5 0.5] /N 1 >> ] /Bounds [ 4.00005] /Encode [0 1 0 1] >> /Extend [false false] >> >> /Subtype /Form << Introduction to (nonconvex) optimization On the other hand, complex optimization problems that cannot be tackled via traditional mathematical programming techniques are commonly solved with AI-based optimization approaches such as the metaheuristics. Then, this session introduces (or reminds) some basics on optimization, and illustrate some key applications in supervised clas-sification. endobj The problem of Clustering has been approached from different disciplines during the last few year’s. An Introduction to Supervised Learning. endobj Because these elds typically give rise to very large instances, rst-order optimization (gradient-based) methods are typically preferred. /Filter /FlateDecode /Subtype /Link �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( A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniquesin optimization that encompass the broadness and diversity of the methods (traditional and … /Subtype /Link * To become familiar with literature of optimization for "data science". /ProcSet [ /PDF ] 98 0 obj endobj /Resources 60 0 R /Type /XObject * To become familiar with literature of optimization for "data science… /Resources 57 0 R 12, No. /Resources 69 0 R >> /Filter /FlateDecode /Subtype /Link Introduction to (nonconvex) optimization Mathematical Optimization has played a crucial role across the three main pillars of Data Science, namely Supervised Learning, Unsupervised Learning and Information Visualization. << 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 3. 1 0 obj endobj /Type /Page 34 0 obj /Type /XObject /Type /Annot Behind numerous standard models and constructions in Data Science there is mathematics that makes things work. << endstream << x���P(�� �� 102 0 obj >> 45 0 obj /Rect [23.246 8.966 73.405 19.201] 77 0 obj >> >> MIP’s are linear optimization programs where some variables are allowed to be integers while others are not once a solution has been obtained. The 54 full papers presented were carefully reviewed and selected from 158 submissions. E(Z�Q4��,W������~�����! Bayesian optimization Bayes rule P(hypothesisjData) = P(Datajhypothesis)P(hypothesis) P(Data) P(hypothesis) is a prior, P(hypothesisjData) is the posterior probability given Data Given Data, we use Bayes rule to infer P(hypothesisjData) Global optimization Problems of derivative-free … /ProcSet [ /PDF ] /ColorSpace 3 0 R /Pattern 2 0 R /ExtGState 1 0 R >> 38 0 obj << /S /GoTo /D (Outline0.1) >> /Resources 53 0 R /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] >> >> endobj endobj Optimization for Data Science Master 2 Data Science, Univ. /Filter /FlateDecode We present a new Bayesian optimization method, environmental entropy search (EnvES), suited for optimizing the hyperparameters of machine learning algorithms on large datasets. question and discussion ** All presentations are in Panorama Room, Third … For the demonstration purpose, imagine following graphical representation for the cost function. /FormType 1 /Shading << /Sh << /ShadingType 3 /ColorSpace /DeviceRGB /Domain [0.0 8.00009] /Coords [8.00009 8.00009 0.0 8.00009 8.00009 8.00009] /Function << /FunctionType 3 /Domain [0.0 8.00009] /Functions [ << /FunctionType 2 /Domain [0.0 8.00009] /C0 [0.5 0.5 0.5] /C1 [0.5 0.5 0.5] /N 1 >> << /FunctionType 2 /Domain [0.0 8.00009] /C0 [0.5 0.5 0.5] /C1 [1 1 1] /N 1 >> ] /Bounds [ 4.00005] /Encode [0 1 0 1] >> /Extend [true false] >> >> endobj In many ways, working with MTN’s data science lead closely resembled the type of interactions I have at Microsoft with my coworkers. /Type /XObject 53 0 obj Optimization provides a powerfultoolboxfor solving data analysis and learning problems. ��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���� ��. This special issue presents nine original, high-quality articles, clearly focused on theoretical and practical aspects of the interaction between artificial intelligence and data science in scientific programming, including cutting-edge topics about optimization, machine learning, recommender systems, metaheuristics, classification, recognition, and real-world application cases. <>>> }�] �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*?��@��� /Subtype /Form /Matrix [1 0 0 1 0 0] /ProcSet [ /PDF /Text ] Other relevant examples in data science 6 Limits and errors of learning. /Length 15 << /S /GoTo /D (Outline0.4) >> /Matrix [1 0 0 1 0 0] 21 0 obj /Length 15 /Resources 94 0 R DATA SCIENCE OPTIMIZATION COMPANY OVERVIEW Tata Group is an Indian multinational conglomerate company headquartered in Mumbai, India. 60 0 obj /BBox [0 0 12.606 12.606] /Type /Annot endobj /Border[0 0 0]/H/N/C[.5 .5 .5] endobj /ProcSet [ /PDF ] /A << /S /GoTo /D (Navigation2) >> x���P(�� �� Optimization Problem. /MediaBox [0 0 362.835 272.126] Stephen Wright (UW-Madison) Optimization Algorithms for Data … 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 3. 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 /Subtype /Link << /Type /XObject Rejoinder to the discussion of “A review of data science in business and industry and a future view by G. Vicario and S. Coleman” Grazia Vicario Shirley Coleman endobj %PDF-1.5 Data Science - Convex optimization and application Summary We begin by some illustrations in challenging topics in modern data science. /Border[0 0 0]/H/N/C[.5 .5 .5] (Subgradient methods) /Border[0 0 0]/H/N/C[.5 .5 .5] 68 0 obj The first is overfitting. endobj /D [51 0 R /XYZ 9.909 273.126 null] >> Wright (UW-Madison) Optimization in Data … Clustering is the process of organizing similar objects into groups, with its main objective of organizing a collection of data items into some meaningful groups. endstream /Rect [23.246 51.7 138.33 61.935] /Filter /FlateDecode /Subtype /Link 56 0 obj << The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science … /Type /Annot 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. 96 0 obj endobj /Type /Annot /XObject << /Fm3 56 0 R /Fm4 58 0 R /Fm2 54 0 R >> /MediaBox [0 0 362.835 272.126] Greedy algorithms often provide an adequate though often not optimal solution. xڵW�o�6~�_�G�8R�$r�[:�E�!��>{Pd��`K�$����ɢ��h��)�?~w� �"��3r1R)�O`!��),Ci�b��Uh3�� Presentation outline 1 Introduction to (convex) optimization models in data science: Classical examples 2 Convexity and nonsmooth calculus tools for optimization. Some old lines of optimization … Querying big data is challenging yet crucial for any business. >> The goal for optimization algorithm is to find parameter values which correspond to minimum value of cost function. /Subtype /Link Numerical optimization … >> >> /Parent 67 0 R 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�,#!���*��. 6, pp. >> /D [95 0 R /XYZ 9.909 273.126 null] 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. /Border[0 0 0]/H/N/C[.5 .5 .5] /Subtype /Link /Type /XObject /ProcSet [ /PDF ] ��K���N�xڣ=��sx98=�t�W��u~�<9����p�rj��"!1�FYp3I��{�R}�n�O�Ru�n����.۲��[���}�v�e�wYk�uV#x��hֲ�[AW"����. << Many problems of practical importance can be formulated as optimization problems. IBM Decision Optimization and Data Science 3 More often, however, a decision optimization application is used as an interactive decision support tool by the decision maker in a what-if iterative process that provides a specific solution or a set of candidate solutions. 62 0 obj endobj ����yx�,���Ҫ���o,>h"�g1�[ut9�0u���۝���Ϫ�to�^��}�we}r�/. /Type /Annot Apparently, for gradient descent to converge to optimal minimum, cost function should be convex. /Type /Annot ARPN Journal of Engineering and Techniques in the Field of Data Mining and Genetic Applied Sciences. >> Vol. He has a Ph.D. from the University of Illinois at Urbana Champaign. endstream /A << /S /GoTo /D (Navigation208) >> -�d�[d�,����,0g�;0��v�P�ֽ��֭R�k7u[��3=T:׋��B(4��{�dSs� L2u�S� ���� ��g�Ñ�xz��j�⧞K�/�>��w�N���BzC %���� /FormType 1 << 75 0 obj /BBox [0 0 12.606 12.606] Presentation outline 1 Introduction to (convex) optimization models in data science: Classical examples 2 Convexity and nonsmooth calculus tools for optimization. >> /Shading << /Sh << /ShadingType 2 /ColorSpace /DeviceRGB /Domain [0 1] /Coords [0 0.0 0 3.9851] /Function << /FunctionType 2 /Domain [0 1] /C0 [1 1 1] /C1 [0.5 0.5 0.5] /N 1 >> /Extend [false false] >> >> Optimization for Data Science 2 Optimization for Data Science Unconstrained nonlinear optimization Constrained pipeline optimization, hyperparameter optimization, data science, machine learning, genetic programming, Pareto op-timization, Python 1. endobj /Border[0 0 0]/H/N/C[.5 .5 .5] Currently, cost-efficient production of Taxol and its analogs remains limited. endobj /D [51 0 R /XYZ 10.909 270.333 null] Complexity of optimization problems & Optimal methods for convex optimization problems 92 0 obj /Type /XObject << >> Master 2 Data Science, Institut Polytechnique de Paris (IPP) 2 References for todays class Amir Beck and Marc Teboulle (2009), SIAM J. 69 0 obj /Length 15 25 0 obj << Other relevant examples in data science 6 Limits and errors of learning. /BBox [0 0 16 16] /Border[0 0 0]/H/N/C[.5 .5 .5] stream Offered by National Research University Higher School of Economics. endobj stream 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 /Subtype /Form Huge amounts of data are collected, routinely and continuously. << /A << /S /GoTo /D (Navigation175) >> 74 0 obj /Subtype /Link endstream /Rect [23.246 28.212 138.421 40.568] %PDF-1.5 /ColorSpace 3 0 R /Pattern 2 0 R /ExtGState 1 0 R stream endstream endobj x���P(�� �� x���P(�� �� Free pdf online ! References for this class Convex Optimization … /Type /Annot View Optimization_1.pdf from CS MISC at Indian Institute of Management, Lucknow. /A << /S /GoTo /D (Navigation77) >> /Matrix [1 0 0 1 0 0] 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]. 22 0 obj >> << /Rect [9.913 198.379 80.421 207.341] /Type /Annot Optimization for Data Science 2 Optimization for Data Science Unconstrained nonlinear optimization Constrained �q�^Y�nj�3�p << /S /GoTo /D (Outline0.9) >> /Type /Annot 42 0 obj Related: Why Germany did not defeat Brazil in the final, or Data Science … /Rect [23.246 177.012 121.966 189.368] 17 0 obj stream 3 0 obj /FormType 1 /Type /Annot The data warehouses traditionally built with On-line Transaction Processing p. cm. /Subtype /Form >> >> * To know what is the field of statistical disclosure control or statistical data protection. /FormType 1 /A << /S /GoTo /D (Navigation2) >> * The ability to protect data using any existing technique. /Border[0 0 0]/H/N/C[.5 .5 .5] /Length 1436 /A << /S /GoTo /D (Navigation145) >> /Border[0 0 0]/H/N/C[.5 .5 .5] endobj Modeling and domain-speci c knowledge is vital: \80% of data analysis is spent on the process of cleaning and preparing the data." /Border[0 0 0]/H/N/C[.5 .5 .5] /ProcSet [ /PDF ] /Rect [9.913 92.313 199.3 104.002] /Resources 55 0 R endobj /Font << /F23 99 0 R /F21 66 0 R >> << (Most academic research deals with the other 20%.) >> /Trans << /S /R >> 61 0 obj 79 0 obj /BBox [0 0 362.835 272.126] endobj /Filter /FlateDecode 70 0 obj It encom-passes seven business sectors: … << >> endobj /Subtype /Link I"�Zˈw6�Y� 13 0 obj >> INTRODUCTION Permission to make digital or hard … (Other topics not covered) endobj stream /Subtype /Link /Border[0 0 0]/H/N/C[.5 .5 .5] endobj << There are two significant problems with MLE in general. << << /S /GoTo /D (Outline0.8) >> Convex optimization and Big Data applications October, 2016 Tata Group was founded in 1868 by Jamsetji Tata as a 1 Data Science 1.1 What is data science : Rates of convergence 3 Subgradient methods 4 Proximal gradient methods 5 Accelerated gradient methods (momentum). << /Border[0 0 0]/H/N/C[.5 .5 .5] stream stream 72 0 obj 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 2. << /S /GoTo /D (Outline0.6) >> /Subtype /Link /Filter /FlateDecode 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]. He enjoys data science and spends time mentoring data scientists, speaking at events, and having fun with blog posts. 76 0 obj Donoho: 50 Years of Data Science, September 2015. Whom this book is for. 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. << /S /GoTo /D (Outline0.3) >> /Type /Annot >> /Font << /F20 65 0 R /F21 66 0 R >> 95 0 obj 63 0 obj /ProcSet [ /PDF ] /Filter /FlateDecode The “no free lunch” of Optimization Specialize Logistic Regression. endobj 29 0 obj endobj The company’s data scientists pull data from Instagram as well as its owner, Facebook , which has exhaustive web-tracking infrastructure and detailed information on many users, including age and education. Taxol (paclitaxel) is a potent anticancer drug first isolated from the Taxus brevifolia Pacific yew tree. Sébastien Bubeck (2015) Convex Optimization… << >> endobj << Optimization for Data Science Fall 2018 Stephen Vavasis August 1, 2018 Course Goals The course will cover optimization techniques used especially for machine learning and data science. endobj /A << /S /GoTo /D (Navigation112) >> 49 0 obj Optimization is hard (in general) Need assumptions! << << /S /GoTo /D (Outline0.10) >> /Type /Annot endobj endobj endobj He has a Ph.D. from the University of Illinois at Urbana Champaign. Evolutionary Computation, Optimization and Learning Algorithms for Data Science Farid Ghareh Mohammadi1, M. Hadi Amini2, and Hamid R. Arabnia1 1: Department of Computer Science, Franklin … * To know software for data protection. endobj endstream endobj The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. /Type /Annot /Resources 59 0 R 116 0 obj /Subtype /Link /BBox [0 0 8 8] stream View Optimization_1.pdf from CS MISC at Indian Institute of Management, Lucknow. /Contents 61 0 R /Filter /FlateDecode << 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 [0.75294 0.82156 0.85588] /C1 [0.4706 0.61766 0.69118] /N 1 >> << /FunctionType 2 /Domain [0.0 6.3031] /C0 [0.4706 0.61766 0.69118] /C1 [0.2853 0.40883 0.4706] /N 1 >> << /FunctionType 2 /Domain [0.0 6.3031] /C0 [0.2853 0.40883 0.4706] /C1 [0.23236 0.32059 0.36472] /N 1 >> << /FunctionType 2 /Domain [0.0 6.3031] /C0 [0.23236 0.32059 0.36472] /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] >> >> (Accelerated gradient methods \(momentum\). He enjoys data science and spends time mentoring data scientists, speaking at events, and having fun with blog posts. 81 0 obj /Matrix [1 0 0 1 0 0] 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 Age of \Big Data" New \Data Science Centers" at many institutions, new degree programs (e.g. Masters in Data Science), new funding initiatives. Data are collected, routinely and continuously “ no free lunch ” of optimization Specialize Logistic Regression tracking! Provide optimal solutions avoiding consumption of many computational resources } r�/ production of taxol and analogs... These elds typically give rise to very large instances, rst-order optimization ( gradient-based ) methods are preferred., energy, Consumer products and chemicals to optimal minimum, cost function basics on optimization, and some. Is important to understand it to be successful in data Science 6 Limits and errors of learning from CS at... Data set becomes larger, high accuracy becomes less critical University of Illinois at Urbana Champaign often great... Summary we begin by some illustrations in challenging topics in modern data Science,... Disclosure control or statistical data protection 5 Accelerated gradient methods 5 Accelerated gradient methods ( momentum.! Science optimization COMPANY OVERVIEW Tata Group is an Indian multinational conglomerate COMPANY in! Yet crucial for any business * �l�Z ` $ �l��n�T�_�VA�f��l� '' �Ë�'/s�G������ �C�����. Data analysis and learning problems parameter values which correspond to minimum value of cost.. To security and optimization basics on optimization, and illustrate some key applications in clas-sification. Academic research deals with the other problem with MLE is the field of Mining... Research deals with the applications in data Science to understand it to successful. Problem of actually calculating the optimal θ correspond to minimum value of cost function be! Applied SCIENCES programming really often yields great results some random initial values for parameters extrapolates. 20 %. from CS MISC at Indian Institute of Management, Lucknow two problems... Are two significant problems with MLE in general } r�/, imagine following graphical representation for the cost function �we... Optimal θ $ �l��n�T�_�VA�f��l� '' �Ë�'/s�G������ > �C����� the “ no free lunch ” of optimization for data,. Greedy algorithms often provide an adequate though often not optimal solution … Convex!, energy, Consumer products and chemicals processing 1 Convex optimization and application Summary we begin by some in. School of Economics – { ��A�^'� & Ѝѓ ��� hN�V * �l�Z ` $ �l��n�T�_�VA�f��l� '' �Ë�'/s�G������ >?. ( in general mass tolerance of better than 0.2 %. — ( information. Statistical disclosure control or statistical data protection for Linear Inverse problems it being done by machine learning researchers of... ) Need assumptions and probabilistically extrapolates their performance to reason about performance the. Of engineering and Techniques in the field of statistical disclosure control or statistical data protection 1868 Jamsetji. �����X�ɚ�-1 ] – { ��A�^'� & Ѝѓ ��� hN�V * �l�Z ` $ �l��n�T�_�VA�f��l� '' �Ë�'/s�G������ �C�����. Relevant examples in data Science Gasnikov Alexander gasnikov.av @ mipt.ru Lecture 2 gradient descent to converge to minimum... `` data Science - Convex optimization for data Science ), new initiatives. Challenging topics in modern data Science 6 Limits and errors of learning the problem! Yew tree Applied SCIENCES often yields great results thesis, we present several contributions of large optimization. Optimization and big data applications October, 2016 1 Convex optimization for `` data Science Gasnikov Alexander gasnikov.av @ Lecture! Brevifolia Pacific yew tree ability to protect data using any existing technique energy, Consumer products and chemicals with! Has been approached from different disciplines during the last few year ’ s in the field of data.! Solving problems of numerical and combinatorial optimization problems for the cost function match requires a mass of. 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