optimization for data science pdf

%PDF-1.5 102 0 obj 49 0 obj /Filter /FlateDecode 51 0 obj << endobj << /S /GoTo /D (Outline0.5) >> 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�. Many problems of practical importance can be formulated as optimization problems. endobj /Matrix [1 0 0 1 0 0] endobj >> /Type /Annot p. cm. endobj >> F��{(1�����29s���oV�)# u View Optimization_1.pdf from CS MISC at Indian Institute of Management, Lucknow. 34 0 obj 52 0 obj x���P(�� �� endobj /Rect [23.246 8.966 73.405 19.201] 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]. << >> /Border[0 0 0]/H/N/C[.5 .5 .5] >> He enjoys data science and spends time mentoring data scientists, speaking at events, and having fun with blog posts. /Length 1175 >> The data warehouses traditionally built with On-line Transaction Processing /ColorSpace 3 0 R /Pattern 2 0 R /ExtGState 1 0 R * To know software for data protection. << Optimization for Data Science 2 Optimization for Data Science Unconstrained nonlinear optimization Constrained 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 x���P(�� �� Complexity of optimization problems & Optimal methods for convex optimization problems E(Z�Q4��,W������~�����! << 30 0 obj endstream /Matrix [1 0 0 1 0 0] For a data set with 36 matches from72 mass values, a significant match can be obtained even when the mass tolerance approaches 1%. /Type /Annot >> /ProcSet [ /PDF ] /Filter /FlateDecode endobj /Border[0 0 0]/H/N/C[.5 .5 .5] endobj /D [95 0 R /XYZ 9.909 273.126 null] endobj endobj endstream endobj endobj 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. /Type /Page endobj << * The ability to protect data using any existing technique. x��YKs�4��Wh�,"��$vpy�7;`a��Ll��S Other relevant examples in data science 6 Limits and errors of learning. >> << /S /GoTo /D (Outline0.10) >> /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] >> >> /Length 15 << /S /GoTo /D (Outline0.4) >> /Filter /FlateDecode <>>> /Filter /FlateDecode 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. 1706-1712, 2017. ���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 Optimization is hard (in general) Need assumptions! An Introduction to Supervised Learning. 56 0 obj 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 2. Optimization is hard (in general) Need assumptions! << pipeline optimization, hyperparameter optimization, data science, machine learning, genetic programming, Pareto op-timization, Python 1. 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]. /Rect [23.246 177.012 121.966 189.368] Introduction to (nonconvex) optimization endobj >> 58 0 obj /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] >> >> Optimization for Data Science 2 Optimization for Data Science Unconstrained nonlinear optimization Constrained In this thesis, we present several contributions of large scale optimization methods with the applications in data science and machine learning. << 10 0 obj DATA SCIENCE OPTIMIZATION COMPANY OVERVIEW Tata Group is an Indian multinational conglomerate company headquartered in Mumbai, India. Rates of convergence) 57 0 obj stream 70 0 obj /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] >> >> Modeling and domain-speci c knowledge is vital: \80% of data analysis is spent on the process of cleaning and preparing the data." /Subtype /Link << 53 0 obj 92 0 obj /Subtype /Link >> /Font << /F20 65 0 R /F21 66 0 R >> endobj /Trans << /S /R >> 2 0 obj >> Other relevant examples in data science 6 Limits and errors of learning. 17 0 obj (Other topics not covered) /Border[0 0 0]/H/N/C[.5 .5 .5] endobj There are two significant problems with MLE in general. 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 /Subtype /Link 59 0 obj Table: Sample of Trip Duration Data (cleaned) used for the model Part 3: Methods. /Rect [23.246 105.256 352.922 118.218] /Resources 57 0 R /D [51 0 R /XYZ 10.909 270.333 null] /Rect [23.246 135.861 352.922 148.824] 93 0 obj /Type /Annot x���P(�� �� -�d�[d�,����,0g�;0��v�P�ֽ��֭R�k7u[��3=T:׋��B(4��{�dSs� L2u�S� ���� ��g�Ñ�xz��j�⧞K�/�>��w�N���BzC endobj 75 0 obj 94 0 obj << 77 0 obj (Most academic research deals with the other 20%.) << Then, this session introduces (or reminds) some basics on optimization, and illustrate some key applications in supervised clas-sification. /Type /Annot Using the demand and trip duration data, a Mixed Integer Programming (MIP) model was developed to find the optimal driving schedule for drivers. << /Rect [9.913 125.039 92.633 134.608] /BBox [0 0 362.835 3.985] /Filter /FlateDecode << INTRODUCTION Permission to make digital or hard … Organizations adopt different databases for big data which is huge in volume and have different data models. MIP’s are linear optimization programs where some variables are allowed to be integers while others are not once a solution has been obtained. /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 (Outline0.8) >> /Type /Annot /Border[0 0 0]/H/N/C[.5 .5 .5] For the demonstration purpose, imagine following graphical representation for the cost function. (Stochastic gradient descent) /Type /Annot << 78 0 obj << Tata Group was founded in 1868 by Jamsetji Tata as a 103 0 obj Currently, cost-efficient production of Taxol and its analogs remains limited. /Type /Annot 54 0 obj 1 Data Science 1.1 What is data science : IMAGING SCIENCES, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems. endobj 22 0 obj 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 … /Resources 69 0 R His report outlined six points for a university to follow in developing a data analyst curriculum. 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 Rates of convergence 3 Subgradient methods 4 Proximal gradient methods 5 Accelerated gradient methods (momentum). 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. Optimization for Data Science Master 2 Data Science, Univ. 100 0 obj << 33 0 obj /ColorSpace 3 0 R /Pattern 2 0 R /ExtGState 1 0 R /MediaBox [0 0 362.835 272.126] 62 0 obj >> /BBox [0 0 12.606 12.606] /Filter /FlateDecode 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. DATA SCIENCE OPTIMIZATION COMPANY OVERVIEW Tata Group is an Indian multinational conglomerate company headquartered in Mumbai, India. Why big data tracking and monitoring is essential to security and optimization. /Border[0 0 0]/H/N/C[.5 .5 .5] 71 0 obj /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] >> >> question and discussion ** All presentations are in Panorama Room, Third … 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 … /D [51 0 R /XYZ 9.909 273.126 null] 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, /FormType 1 ����yx�,���Ҫ���o,>h"�g1�[ut9�0u���۝���Ϫ�to�^��}�we}r�/. 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. /A << /S /GoTo /D (Navigation77) >> 60 0 obj As the data set becomes larger, high accuracy becomes less critical. endobj /Contents 96 0 R — (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. (Convexity and nonsmooth calculus tools for optimization. >> /Resources 55 0 R Q܋���qP������k�2/�#O�q������� ��^���#�(��s��8�"�����/@;����ʺsY�N��V���P2�s| << %PDF-1.5 It will be of particular interest to the data science, computer science, optimization… /Type /XObject << /S /GoTo /D [51 0 R /Fit] >> 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… << View Lecture20.pdf from CS 794 at University of Waterloo. 4 0 obj endobj /Resources 53 0 R His report outlined six points for a university to follow in developing a data … << 97 0 obj /Filter /FlateDecode endobj /Subtype /Form (References) endobj << /S /GoTo /D (Outline0.2) >> I"�Zˈw6�Y� << /Type /Annot With a smaller data set, 13 matches from 24, a significant match requires a mass tolerance of better than 0.2%. /Type /XObject endstream (Introduction to \(convex\) optimization models in data science: Classical examples) endobj /Rect [9.913 231.106 66.299 242.795] 3 0 obj endstream /BBox [0 0 5669.291 8] 73 0 obj 76 0 obj /Matrix [1 0 0 1 0 0] Stochastic gradient descent (SGD) is the simplest optimization algorithm used to find parameters which minimizes the given cost function. /ProcSet [ /PDF ] The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science … 26 0 obj The “no free lunch” of Optimization Specialize Logistic Regression. It is important to understand it to be successful in Data Science. 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 3. In this specialisation we will cover wide range of mathematical tools and see how they arise in Data Science. endobj /Border[0 0 0]/H/N/C[.5 .5 .5] /Subtype /Form >> 95 0 obj The 54 full papers presented were carefully reviewed and selected from 158 submissions. }�] �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*?��@��� /Type /Annot 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. /Subtype /Link /Border[0 0 0]/H/N/C[.5 .5 .5] Complexity of optimization problems & Optimal methods for convex optimization problems Masters in Data Science), new funding initiatives. stream 81 0 obj endobj The 54 full papers presented were carefully reviewed and selected from 158 submissions. /XObject << /Fm3 56 0 R /Fm4 58 0 R /Fm2 54 0 R >> Related: Why Germany did not defeat Brazil in the final, or Data Science … endobj /Border[0 0 0]/H/N/C[.5 .5 .5] << /S /GoTo /D (Outline0.6) >> endobj /ProcSet [ /PDF ] /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] >> >> /Type /XObject Apparently, for gradient descent to converge to optimal minimum, cost function should be convex. 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. Paris Saclay Robert M. Gower & ... Optimisation for Data Science. These approaches provide optimal solutions avoiding consumption of many computational resources. Optimization provides a powerfultoolboxfor solving data analysis and learning problems. /Resources 93 0 R 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 … 98 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. 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. << 38 0 obj /Subtype /Form stream << We start with defining some random initial values for parameters. endobj /A << /S /GoTo /D (Navigation2) >> << endobj /A << /S /GoTo /D (Navigation229) >> /Parent 67 0 R (peter.nystrup{at}matstat.lu.se) 2. 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. << endobj * To know what is the field of statistical disclosure control or statistical data protection. In many ways, working with MTN’s data science lead closely resembled the type of interactions I have at Microsoft with my coworkers. He has a Ph.D. from the University of Illinois at Urbana Champaign. 42 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 >> endobj >> endobj 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 45 0 obj 69 0 obj /A << /S /GoTo /D (Navigation208) >> [Dasu and Johnson, 2003]. /Rect [23.246 211.928 352.922 224.284] Sébastien Bubeck (2015) Convex Optimization… /Subtype /Form Donoho: 50 Years of Data Science, September 2015. /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] >> >> /MediaBox [0 0 362.835 272.126] Convex optimization and Big Data applications October, 2016 << >> 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 … endstream >> Numerical optimization … /FormType 1 In the first part, we present new computational methods and associated computational guarantees for solving convex optimization … << 1 Data Science 1.1 What is data science : Greedy algorithms often provide an adequate though often not optimal solution. /Border[0 0 0]/H/N/C[.5 .5 .5] /FormType 1 Introduction to (nonconvex) optimization /Matrix [1 0 0 1 0 0] 2018 Conference on Optimization and Data Science Program Schedule * Each talk includes 30 Min. endobj << 64 0 obj 46 0 obj endobj << Some old lines of optimization … /A << /S /GoTo /D (Navigation175) >> /Border[0 0 0]/H/N/C[.5 .5 .5] /Type /XObject /Resources 94 0 R 29 0 obj stream /Resources 60 0 R 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 * The ability to protect data using any existing technique. /A << /S /GoTo /D (Navigation22) >> << endobj >> endobj 12, No. I Consumer and citizen data… It turned out that the recursive-dbscan algorithm greatly outperformed the Google Optimization Tools method. endobj /A << /S /GoTo /D (Navigation2) >> 37 0 obj 21 0 obj /Length 15 <>/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>> Huge amounts of data are collected, routinely and continuously. ... universal optimization method. << /Rect [23.246 155.645 148.269 168.001] /Rect [23.246 51.7 138.33 61.935] Data Science - Convex optimization and application Summary We begin by some illustrations in challenging topics in modern data science. /ProcSet [ /PDF /Text ] 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 << /S /GoTo /D (Outline0.3) >> /ProcSet [ /PDF ] /FormType 1 * To know what is the field of statistical disclosure control or statistical data protection. << /Subtype /Link The other problem with MLE is the logistical problem of actually calculating the optimal θ. >> endobj /Rect [23.246 70.946 150.602 83.302] (Noise reduction methods) >> We present a new Bayesian optimization method, environmental entropy search (EnvES), suited for optimizing the hyperparameters of machine learning algorithms on large datasets. /FormType 1 ��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���� ��. stream The first is overfitting. 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. <> Querying big data is challenging yet crucial for any business. >> /Resources 82 0 R endstream /Rect [23.246 28.212 138.421 40.568] /Length 15 Lastly, for the Ugandan Revenue Authority, they had an interest in data science … Introduction to \(nonconvex\) optimization models in supervised machine learning) << /S /GoTo /D (Outline0.7) >> 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 3. 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. Wright (UW-Madison) Optimization in Data … /Subtype /Link endobj 61 0 obj stream The particular requirements of data analysis problems are driving new research in optimization | much of it being done by machine learning researchers. /Type /XObject /ProcSet [ /PDF ] 68 0 obj /Subtype /Link He enjoys data science and spends time mentoring data scientists, speaking at events, and having fun with blog posts. Many algorithms have been developed in recent years for solving problems of numerical and combinatorial optimization problems. /Filter /FlateDecode x���P(�� �� endobj endobj /Trans << /S /R >> >> Other relevant examples in data science) endobj 1. %���� << << Optimization for Data Science Lecture 20: Robust Linear Regression Kimon Fountoulakis School of Computer Science University of Very large instances, rst-order optimization ( gradient-based ) methods are typically preferred optimization | much of it being by. Performance to reason about performance on the entire dataset the applications in clas-sification... Disciplines during the last few year ’ s great results the logistical problem of Clustering been! Urbana Champaign of Economics hard, dynamic programming really often yields great results 3 Subgradient methods Proximal! Science… View Lecture20.pdf from CS 794 at University of Illinois at Urbana Champaign optimization problems (. 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