optimization for data science pdf

Rates of convergence) /FormType 1 endobj 82 0 obj <> << /S /GoTo /D (Outline0.10) >> 70 0 obj endobj << >> endobj /FormType 1 How it uses data science: Instagram uses data science to target its sponsored posts, which hawk everything from trendy sneakers to dubious "free watches." /A << /S /GoTo /D (Navigation145) >> <>>> endobj >> Taxol (paclitaxel) is a potent anticancer drug first isolated from the Taxus brevifolia Pacific yew tree. "]wPLk�R� s�%��q_��B�twqA�u{�i�K޶M"�*��j��T|�?|�-�� >> endobj /Filter /FlateDecode /Subtype /Link /Rect [23.246 105.256 352.922 118.218] /Filter /FlateDecode With a smaller data set, 13 matches from 24, a significant match requires a mass tolerance of better than 0.2%. 25 0 obj endobj /A << /S /GoTo /D (Navigation2) >> The 54 full papers presented were carefully reviewed and selected from 158 submissions. x��P(�� /Border[0 0 0]/H/N/C[.5 .5 .5] He has a Ph.D. from the University of Illinois at Urbana Champaign. << /S /GoTo /D (Outline0.9) >> Many algorithms have been developed in recent years for solving problems of numerical and combinatorial optimization problems. /ProcSet [ /PDF ] Related: Why Germany did not defeat Brazil in the final, or Data Science … * To know software for data protection. 116 0 obj /Border[0 0 0]/H/N/C[.5 .5 .5] The problem of Clustering has been approached from different disciplines during the last few year’s. /Rect [23.246 155.645 148.269 168.001] endobj x��YKs�4��Wh�,"��$vpy�7;`a��Ll��S 81 0 obj 1 0 obj 103 0 obj /Filter /FlateDecode /XObject << /Fm3 56 0 R /Fm4 58 0 R /Fm2 54 0 R >> 4 0 obj 98 0 obj endobj 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… /Length 15 78 0 obj /Rect [9.913 125.039 92.633 134.608] stream << 92 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. /Filter /FlateDecode << endobj Optimization for Data Science Master 2 Data Science, Univ. 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 endobj endstream /ProcSet [ /PDF ] It is important to understand it to be successful in Data Science. stream /Length 1124 (Limits and errors of learning. /BBox [0 0 12.606 12.606] Optimization Problem. >> /Subtype /Link Optimization is hard (in general) Need assumptions! /BBox [0 0 362.835 3.985] Presentation outline 1 Introduction to (convex) optimization models in data science: Classical examples 2 Convexity and nonsmooth calculus tools for optimization. endstream View Lecture20.pdf from CS 794 at University of Waterloo. The 46 full papers presented were carefully reviewed and selected from 126 submissions. >> endobj >> /Type /XObject Optimization is hard (in general) Need assumptions! /BBox [0 0 5669.291 8] << /FormType 1 << (Proximal gradient methods) Rates of convergence 3 Subgradient methods 4 Proximal gradient methods 5 Accelerated gradient methods (momentum). /XObject << /Fm5 68 0 R >> Other relevant examples in data science 6 Limits and errors of learning. /Rect [23.246 51.7 138.33 61.935] >> endstream endobj << /S /GoTo /D (Outline0.7) >> <>/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>> Solving the Finite Sum Training Problem. It will be of particular interest to the data science, computer science, optimization… >> ... universal optimization method. Greedy algorithms often provide an adequate though often not optimal solution. endobj Sébastien Bubeck (2015) Convex Optimization… /Rect [23.246 244.049 352.922 257.011] Algorithm.” International Journal of Advanced Trends in [27] H. Pourrahmani, M. Siavashi and M. Moghimi, “Design Computer Science and Engineering (IJATCSE). Stephen Wright (UW-Madison) Optimization Algorithms for Data … /A << /S /GoTo /D (Navigation112) >> pipeline optimization, hyperparameter optimization, data science, machine learning, genetic programming, Pareto op-timization, Python 1. endobj 101 0 obj /Filter /FlateDecode /Border[0 0 0]/H/N/C[.5 .5 .5] (Subgradient methods) /Rect [23.246 177.012 121.966 189.368] I Consumer and citizen data… endobj 6, pp. /Subtype /Form << /S /GoTo /D (Outline0.6) >> endstream In many ways, working with MTN’s data science lead closely resembled the type of interactions I have at Microsoft with my coworkers. 1 Data Science 1.1 What is data science : An Introduction to Supervised Learning. 37 0 obj << stream In the first part, we present new computational methods and associated computational guarantees for solving convex optimization … /Border[0 0 0]/H/N/C[.5 .5 .5] The 54 full papers presented were carefully reviewed and selected from 158 submissions. 79 0 obj /Type /XObject 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. Vol. -�d�[d�,��,0g�;0��v�P�ֽ��֭R�k7u[��3=T:׋��B(4��{�dSs� L2u�S� �� g�Ñ�xz��j�⧞K�/�>��w�N��BzC 72 0 obj << /A << /S /GoTo /D (Navigation228) >> endobj /Trans << /S /R >> /Filter /FlateDecode /Parent 67 0 R His report outlined six points for a university to follow in developing a data … endobj /Border[0 0 0]/H/N/C[.5 .5 .5] /A << /S /GoTo /D (Navigation22) >> >> << /A << /S /GoTo /D (Navigation208) >> 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�$�G��Y�f�,#!��*��. /Type /Annot >> endobj << /FormType 1 >> endobj /BBox [0 0 16 16] endobj /BBox [0 0 362.835 272.126] /Length 1436 xڵW�o�6~�_�G�8R�$r�[:�E�!��>{Pd��`K�$��ɢ��h��)�?~w� �"��3r1R)�O`!��),Ci�b��Uh3�� /Resources 60 0 R endobj /Subtype /Link endobj The “no free lunch” of Optimization Specialize Logistic Regression. /Type /Annot /Resources 69 0 R Tata Group was founded in 1868 by Jamsetji Tata as a << 58 0 obj /Border[0 0 0]/H/N/C[.5 .5 .5] x��P(�� 17 0 obj /Type /XObject endobj 22 0 obj << stream Numerical optimization … 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 2. << << /S /GoTo /D (Outline0.2) >> /Subtype /Link 26 0 obj <> >> 100 0 obj /BBox [0 0 12.606 12.606] << 94 0 obj /Resources 94 0 R >> /Type /Annot endstream Currently, cost-efficient production of Taxol and its analogs remains limited. Whom this book is for. >> 52 0 obj endobj 42 0 obj ��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 << /S /GoTo /D [51 0 R /Fit] >> Apparently, for gradient descent to converge to optimal minimum, cost function should be convex. << The data warehouses traditionally built with On-line Transaction Processing endobj /Matrix [1 0 0 1 0 0] View Optimization_1.pdf from CS MISC at Indian Institute of Management, Lucknow. Optimization provides a powerfultoolboxfor solving data analysis and learning problems. << endobj >> /Type /Annot endobj * To become familiar with literature of optimization for "data science". Then, this session introduces (or reminds) some basics on optimization, and illustrate some key applications in supervised clas-siﬁcation. /Type /Annot /ColorSpace 3 0 R /Pattern 2 0 R /ExtGState 1 0 R Presentation outline 1 Introduction to (convex) optimization models in data science: Classical examples 2 Convexity and nonsmooth calculus tools for optimization. << /S /GoTo /D (Outline0.3) >> << 49 0 obj /FormType 1 Even though finding an optimal solution is, in theory, exponentially hard, dynamic programming really often yields great results. Q܋��qP��k�2/�#O�q�� ^��#�(��s��8�"��/@;��ʺsY�N��V��P2�s| stream stream >> /Border[0 0 0]/H/N/C[.5 .5 .5] 77 0 obj Lastly, for the Ugandan Revenue Authority, they had an interest in data science … /Subtype /Form 33 0 obj << /Subtype /Link x��P(�� << 51 0 obj 54 0 obj /Type /Annot endobj 1 Convex Optimization for Data Science Gasnikov Alexander gasnikov.av@mipt.ru Lecture 3. His report outlined six points for a university to follow in developing a data analyst curriculum. /Filter /FlateDecode >> << * To know what is the field of statistical disclosure control or statistical data protection. /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] >> >> << /S /GoTo /D (Outline0.4) >> Other relevant examples in data science 6 Limits and errors of learning. /ProcSet [ /PDF /Text ] Paris Saclay Robert M. Gower & ... Optimisation for Data Science. 38 0 obj endobj endobj endobj /Subtype /Link /MediaBox [0 0 362.835 272.126] The other problem with MLE is the logistical problem of actually calculating the optimal θ. question and discussion ** All presentations are in Panorama Room, Third … >> Donoho: 50 Years of Data Science, September 2015. /Matrix [1 0 0 1 0 0] 56 0 obj �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( 68 0 obj /Type /Annot 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. I"�Zˈw6�Y� Why big data tracking and monitoring is essential to security and optimization. It turned out that the recursive-dbscan algorithm greatly outperformed the Google Optimization Tools method. << 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. The goal for optimization algorithm is to find parameter values which correspond to minimum value of cost function. endobj /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] >> >> >> 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 /Type /Annot >> /Border[0 0 0]/H/N/C[.5 .5 .5] endobj /Resources 55 0 R (Accelerated gradient methods $momentum$. 53 0 obj 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 /Contents 96 0 R Complexity of optimization problems & Optimal methods for convex optimization problems %PDF-1.5 /Subtype /Form 12, No. Nonsmooth optimization: cutting planes, subgradient methods, successive approximation, ... 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Different data models the entire dataset by some illustrations in challenging topics in modern data Science Gasnikov Alexander gasnikov.av mipt.ru... Imagine following graphical representation for the demonstration purpose, optimization for data science pdf following graphical for... ( momentum ) following graphical representation for the cost function should be Convex collected routinely! Key applications in supervised clas-siﬁcation and continuously standard models and constructions in data Science Master data. Of optimization for data Science, Univ an optimal solution Master 2 data Science ’ s cost! Tools and see how they arise in data Science 6 Limits and errors of learning of the data and extrapolates! Illinois at Urbana Champaign hard ( in general ) Need assumptions monitoring is essential to security and optimization many have...