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He has a Ph.D. from the University of Illinois at Urbana Champaign. >> It is important to understand it to be successful in Data Science. * To know software for data protection. xڵW�o�6~�_�G�8R�$r�[:�E�!��>{Pd��`K�$����ɢ��h��)�?~w� �"��3r1R)�O`!��),Ci�b��Uh3�� 74 0 obj Then, this session introduces (or reminds) some basics on optimization, and illustrate some key applications in supervised clas-sification. endobj /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] >> >> 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 … >> stream 13 0 obj 33 0 obj Optimization is hard (in general) Need assumptions! /Rect [23.246 105.256 352.922 118.218] Related: Why Germany did not defeat Brazil in the final, or Data Science … endobj /Subtype /Link endobj It encom-passes seven business sectors: … (Other topics not covered) Using the demand and trip duration data, a Mixed Integer Programming (MIP) model was developed to find the optimal driving schedule for drivers. endobj /Matrix [1 0 0 1 0 0] The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science … 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 /ProcSet [ /PDF ] 56 0 obj /Filter /FlateDecode /A << /S /GoTo /D (Navigation60) >> 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 2. << /Subtype /Form /A << /S /GoTo /D (Navigation77) >> /Font << /F20 65 0 R /F21 66 0 R >> <> [Dasu and Johnson, 2003]. x���P(�� �� %PDF-1.5 stream << 64 0 obj ����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^� /Rect [23.246 51.7 138.33 61.935] For a data set with 36 matches from72 mass values, a significant match can be obtained even when the mass tolerance approaches 1%. Q܋���qP������k�2/�#O�q������� ��^���#�(��s��8�"�����/@;����ʺsY�N��V���P2�s| 57 0 obj endobj pipeline optimization, hyperparameter optimization, data science, machine learning, genetic programming, Pareto op-timization, Python 1. << x���P(�� �� ��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) 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 … For the demonstration purpose, imagine following graphical representation for the cost function. His report outlined six points for a university to follow in developing a data analyst curriculum. /FormType 1 DATA SCIENCE OPTIMIZATION COMPANY OVERVIEW Tata Group is an Indian multinational conglomerate company headquartered in Mumbai, India. /Subtype /Link /Subtype /Link >> 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 … /MediaBox [0 0 362.835 272.126] /BBox [0 0 12.606 12.606] 59 0 obj << /S /GoTo /D (Outline0.3) >> 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�,#!���*��. /Subtype /Link /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] >> >> endobj endobj /Length 15 His report outlined six points for a university to follow in developing a data … << Wright (UW-Madison) Optimization in Data … >> >> /ColorSpace 3 0 R /Pattern 2 0 R /ExtGState 1 0 R 46 0 obj Introduction to (nonconvex) optimization We start with defining some random initial values for parameters. 76 0 obj EnvES executes fast algorithm runs on subsets of the data and probabilistically extrapolates their performance to reason about performance on the entire dataset. /A << /S /GoTo /D (Navigation112) >> endobj 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. (Most academic research deals with the other 20%.) 38 0 obj endobj /Rect [23.246 28.212 138.421 40.568] /BBox [0 0 362.835 3.985] /BBox [0 0 362.835 272.126] Paris Saclay Robert M. Gower & ... Optimisation for Data Science. * To know software for data protection. * To become familiar with literature of optimization for "data science… /FormType 1 /Resources 94 0 R >> stream >> 1 Data Science 1.1 What is data science : Solving the Finite Sum Training Problem. >> 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 … /Border[0 0 0]/H/N/C[.5 .5 .5] 58 0 obj 63 0 obj Presentation outline 1 Introduction to (convex) optimization models in data science: Classical examples 2 Convexity and nonsmooth calculus tools for optimization. 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]. Other relevant examples in data science 6 Limits and errors of learning. Many problems of practical importance can be formulated as optimization problems. << * To know what is the field of statistical disclosure control or statistical data protection. /ProcSet [ /PDF ] /Filter /FlateDecode endobj 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. /Type /XObject ARPN Journal of Engineering and Techniques in the Field of Data Mining and Genetic Applied Sciences. The first is overfitting. endobj 2018 Conference on Optimization and Data Science Program Schedule * Each talk includes 30 Min. (Proximal gradient methods) 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. 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. An Introduction to Supervised Learning. endstream << /S /GoTo /D (Outline0.5) >> /Matrix [1 0 0 1 0 0] /Border[0 0 0]/H/N/C[.5 .5 .5] /Border[0 0 0]/H/N/C[.5 .5 .5] << /FormType 1 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 /ProcSet [ /PDF ] /Parent 67 0 R 1 Data Science 1.1 What is data science : The goal for optimization algorithm is to find parameter values which correspond to minimum value of cost function. /Type /Annot 54 0 obj endobj Many algorithms have been developed in recent years for solving problems of numerical and combinatorial optimization problems. (Noise reduction methods) >> /ColorSpace 3 0 R /Pattern 2 0 R /ExtGState 1 0 R Behind numerous standard models and constructions in Data Science there is mathematics that makes things work. (Subgradient methods) In the first part, we present new computational methods and associated computational guarantees for solving convex optimization … ... universal optimization method. Introduction to (nonconvex) optimization /A << /S /GoTo /D (Navigation112) >> /FormType 1 References for this class Convex Optimization … 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 (Outline0.4) >> >> endobj x���P(�� �� << x���P(�� �� >> /Type /XObject 52 0 obj 94 0 obj /A << /S /GoTo /D (Navigation145) >> 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. stream The “no free lunch” of Optimization Specialize Logistic Regression. /Contents 96 0 R << /ProcSet [ /PDF ] /A << /S /GoTo /D (Navigation22) >> endobj /Subtype /Link question and discussion ** All presentations are in Panorama Room, Third … /Matrix [1 0 0 1 0 0] /Filter /FlateDecode /Length 15 This blog is the perfect guide for you to learn all the concepts required to clear a Data Science interview. Introduction to \(nonconvex\) optimization models in supervised machine learning) << >> (Accelerated gradient methods \(momentum\). endstream /Trans << /S /R >> endobj Masters in Data Science), new funding initiatives. }�] �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*?��@��� * The ability to protect data using any existing technique. /Type /Annot 116 0 obj << The data warehouses traditionally built with On-line Transaction Processing endobj (References) /BBox [0 0 8 8] Optimization Problem. endobj presentation and 5 Min. DATA SCIENCE OPTIMIZATION COMPANY OVERVIEW Tata Group is an Indian multinational conglomerate company headquartered in Mumbai, India. 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. 22 0 obj endobj /MediaBox [0 0 362.835 272.126] /Parent 67 0 R Presentation outline 1 Introduction to (convex) optimization models in data science: Classical examples 2 Convexity and nonsmooth calculus tools for optimization. /Border[0 0 0]/H/N/C[.5 .5 .5] /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] >> >> /FormType 1 endobj /Rect [23.246 70.946 150.602 83.302] 70 0 obj Master 2 Data Science, Institut Polytechnique de Paris (IPP) 2 References for todays class Amir Beck and Marc Teboulle (2009), SIAM J. >> endobj /Subtype /Link 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. 60 0 obj /Rect [9.913 198.379 80.421 207.341] 1706-1712, 2017. 34 0 obj 3 0 obj He has a Ph.D. from the University of Illinois at Urbana Champaign. /Border[0 0 0]/H/N/C[.5 .5 .5] >> endstream It will be of particular interest to the data science, computer science, optimization… 6, pp. endobj << /A << /S /GoTo /D (Navigation175) >> INTRODUCTION Permission to make digital or hard … 14 0 obj endobj 101 0 obj 53 0 obj endobj These approaches provide optimal solutions avoiding consumption of many computational resources. Apparently, for gradient descent to converge to optimal minimum, cost function should be convex. 97 0 obj endobj << 1 0 obj Single Chapter PDF Download ... is a very general way to frame a large class of problems in data science. << /S /GoTo /D (Outline0.1) >> << endobj >> (Limits and errors of learning. The 54 full papers presented were carefully reviewed and selected from 158 submissions. /Border[0 0 0]/H/N/C[.5 .5 .5] << /S /GoTo /D (Outline0.8) >> /Subtype /Form Because these elds typically give rise to very large instances, rst-order optimization (gradient-based) methods are typically preferred. endobj << /Filter /FlateDecode * The ability to protect data using any existing technique. endobj Querying big data is challenging yet crucial for any business. 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. 71 0 obj /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] >> >> Rates of convergence 3 Subgradient methods 4 Proximal gradient methods 5 Accelerated gradient methods (momentum). 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. 100 0 obj I"�Zˈw6�Y� endobj stream 103 0 obj /Type /XObject /Subtype /Link The 46 full papers presented were carefully reviewed and selected from 126 submissions. /Subtype /Form View Optimization_1.pdf from CS MISC at Indian Institute of Management, Lucknow. /D [51 0 R /XYZ 9.909 273.126 null] Optimization for Data Science Lecture 20: Robust Linear Regression Kimon Fountoulakis School of Computer Science University of 1. 92 0 obj 95 0 obj >> /Border[0 0 0]/H/N/C[.5 .5 .5] endobj Donoho: 50 Years of Data Science, September 2015. /Subtype /Form /A << /S /GoTo /D (Navigation2) >> There are two significant problems with MLE in general. 18 0 obj x��YKs�4��Wh�,"��$vpy�7;`a��Ll��S Offered by National Research University Higher School of Economics. endstream 102 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. We present a new Bayesian optimization method, environmental entropy search (EnvES), suited for optimizing the hyperparameters of machine learning algorithms on large datasets. << /S /GoTo /D (Outline0.2) >> << /S /GoTo /D (Outline0.6) >> /Filter /FlateDecode /Border[0 0 0]/H/N/C[.5 .5 .5] /Matrix [1 0 0 1 0 0] endobj /BBox [0 0 5669.291 8] << Other relevant examples in data science) /Resources 82 0 R << /Type /Annot endobj 29 0 obj /Matrix [1 0 0 1 0 0] >> Why big data tracking and monitoring is essential to security and optimization. >> /D [95 0 R /XYZ 9.909 273.126 null] /ProcSet [ /PDF ] /Border[0 0 0]/H/N/C[.5 .5 .5] /Rect [9.913 125.039 92.633 134.608] endobj �q�^Y�nj�3�p (Introduction to \(convex\) optimization models in data science: Classical examples) << Vol. >> >> /Type /Annot << /Subtype /Link /Type /Annot endobj /Length 1124 << << View Optimization_1.pdf from CS MISC at Indian Institute of Management, Lucknow. J\bz���A���� �����x�ɚ�-1]–{��A�^'�&Ѝѓ ��� hN�V*�l�Z`$�l��n�T�_�VA�f��l�"�Ë�'/s�G������>�C�����? <> endobj Then, this session introduces (or reminds) some basics on optimization, and illustrate some key applications in supervised clas-sification. 10 0 obj /A << /S /GoTo /D (Navigation22) >> /Length 15 << If the data (Stochastic gradient descent) << /D [95 0 R /XYZ 9.909 273.126 null] Free pdf online ! << /S /GoTo /D (Outline0.9) >> /XObject << /Fm3 56 0 R /Fm4 58 0 R /Fm2 54 0 R >> %���� endobj /Border[0 0 0]/H/N/C[.5 .5 .5] << Convex optimization and Big Data applications October, 2016 /Rect [23.246 211.928 352.922 224.284] /Subtype /Link It encom-passes seven business sectors: communications and information technology, engineering, materials, services, energy, consumer products and chemicals. << /Rect [9.913 92.313 199.3 104.002] endobj In this specialisation we will cover wide range of mathematical tools and see how they arise in Data Science. >> endobj /Resources 59 0 R -�d�[d�,����,0g�;0��v�P�ֽ��֭R�k7u[��3=T:׋��B(4��{�dSs� L2u�S� ���� ��g�Ñ�xz��j�⧞K�/�>��w�N���BzC /D [51 0 R /XYZ 9.909 273.126 null] 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 3. 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. >> /Resources 93 0 R 12, No. >> /D [51 0 R /XYZ 10.909 270.333 null] <>>> 68 0 obj << /Border[0 0 0]/H/N/C[.5 .5 .5] /Subtype /Link 50 0 obj >> /Resources 60 0 R /A << /S /GoTo /D (Navigation145) >> << << /Border[0 0 0]/H/N/C[.5 .5 .5] >> x���P(�� �� /Matrix [1 0 0 1 0 0] >> 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 x���P(�� �� endobj /Type /XObject /XObject << /Fm5 68 0 R >> Complexity of optimization problems & Optimal methods for convex optimization problems 69 0 obj >> /Length 15 /Type /Annot /Type /XObject << /Length 1436 81 0 obj >> /Rect [23.246 135.861 352.922 148.824] /Trans << /S /R >> endobj Optimization for Data Science 2 Optimization for Data Science Unconstrained nonlinear optimization Constrained << "]wPLk�R� s�%���q_�����B�twqA�u{�i�K޶M"�*��j����T|�?|�-�� 42 0 obj << << Some old lines of optimization … 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 other problem with MLE is the logistical problem of actually calculating the optimal θ. F��{(1�����29s���oV�)# u Mathematical Optimization has played a crucial role across the three main pillars of Data Science, namely Supervised Learning, Unsupervised Learning and Information Visualization. endobj %PDF-1.5 An Luong. /ProcSet [ /PDF /Text ] In this thesis, we present several contributions of large scale optimization methods with the applications in data science and machine learning. /Type /XObject endobj << /Font << /F23 99 0 R /F21 66 0 R >> endobj /FormType 1 /Rect [9.913 231.106 66.299 242.795] Rates of convergence 3 Subgradient methods 4 Proximal gradient methods 5 Accelerated gradient methods (momentum). /Type /Annot stream /Subtype /Link Optimization for Data Science Master 2 Data Science, Univ. /Subtype /Link 78 0 obj /A << /S /GoTo /D (Navigation229) >> Organizations adopt different databases for big data which is huge in volume and have different data models. 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 … /Rect [23.246 244.049 352.922 257.011] /Matrix [1 0 0 1 0 0] 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 endobj /Type /Annot << (peter.nystrup{at}matstat.lu.se) 2. 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. >> Complexity of optimization problems & Optimal methods for convex optimization problems /Length 15 >> stream >> /Resources 53 0 R 2 0 obj endstream /Filter /FlateDecode Other relevant examples in data science 6 Limits and errors of learning. endobj * To know what is the field of statistical disclosure control or statistical data protection. << /S /GoTo /D (Outline0.10) >> 25 0 obj << /Filter /FlateDecode Stephen Wright (UW-Madison) Optimization Algorithms for Data … 49 0 obj endobj 61 0 obj The problem of Clustering has been approached from different disciplines during the last few year’s. 79 0 obj << /ProcSet [ /PDF ] 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�. Modeling and domain-speci c knowledge is vital: \80% of data analysis is spent on the process of cleaning and preparing the data." 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 endstream Even though finding an optimal solution is, in theory, exponentially hard, dynamic programming really often yields great results. Sébastien Bubeck (2015) Convex Optimization… He enjoys data science and spends time mentoring data scientists, speaking at events, and having fun with blog posts. /Rect [23.246 177.012 121.966 189.368] /Resources 57 0 R /Rect [23.246 8.966 73.405 19.201] /ProcSet [ /PDF ] Algorithm.” International Journal of Advanced Trends in [27] H. Pourrahmani, M. Siavashi and M. Moghimi, “Design Computer Science and Engineering (IJATCSE). Stochastic gradient descent (SGD) is the simplest optimization algorithm used to find parameters which minimizes the given cost function. Huge amounts of data are collected, routinely and continuously. 62 0 obj stream stream /Subtype /Form endstream 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 3. MIP’s are linear optimization programs where some variables are allowed to be integers while others are not once a solution has been obtained. /Type /Annot /Type /Page 17 0 obj >> << … 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 2. /Length 15 ����yx�,���Ҫ���o,>h"�g1�[ut9�0u���۝���Ϫ�to�^��}�we}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] >> >> 93 0 obj >> endobj /Type /Page 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 75 0 obj View Lecture20.pdf from CS 794 at University of Waterloo. 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