{"id":558,"date":"2015-09-28T15:17:53","date_gmt":"2015-09-28T15:17:53","guid":{"rendered":"http:\/\/sadievrenseker.com\/wp\/?p=558"},"modified":"2016-11-02T16:34:35","modified_gmt":"2016-11-02T16:34:35","slug":"data-mining-course-istanbul-commerce-university","status":"publish","type":"post","link":"https:\/\/sadievrenseker.com\/?p=558","title":{"rendered":"Data Mining Course, Istanbul Commerce University"},"content":{"rendered":"

DERS \u0130ZLENCES\u0130 (Syllabus)<\/p>\n

Veri Madencili\u011fi <\/strong><\/p>\n

Data Mining<\/strong><\/p>\n

14.00 \u2013 17.00 Wed.<\/p>\n

Dersi Veren : Do\u00e7. Dr. \u015eadi Evren \u015eEKER<\/p>\n

Instructor: Dr. \u015eadi Evren \u015eEKER<\/p>\n

E-Mail: datamining@sadievrenseker.com<\/a><\/p>\n

Web Sitesi: http:\/\/sadievrenseker.com\/?p=558<\/a><\/p>\n

Giri\u015f:<\/strong> G\u00fcn\u00fcm\u00fczde Internet\u2019in geli\u015fimine paralel olarak h\u0131zla geli\u015fen trendlerden birisi de gerek internet gerekse di\u011fer veri kaynaklar\u0131 \u00fczerinde i\u015fletilen veri madencili\u011fi \u00e7al\u0131\u015fmalar\u0131d\u0131r. Bu \u00e7al\u0131\u015fmalar\u0131n genel olarak amac\u0131, verinin i\u015flenerek faydal\u0131 sonu\u00e7lar\u0131n \u00e7\u0131kar\u0131lmas\u0131d\u0131r. \u00d6zellikle \u00e7ok b\u00fcy\u00fck miktarlardaki verinin i\u015flenmesi ve bilginin de\u011fi\u015fim h\u0131z\u0131, \u00e7e\u015fitlili\u011fi ve g\u00fcvenilirli\u011fi ile ilgili problemlerin \u00e7\u00f6z\u00fclmesi i\u00e7in g\u00fcncel ve son trend teknolojilerin kat\u0131l\u0131mc\u0131lara kazand\u0131r\u0131lmas\u0131, bununla birlikte klasik veri madencili\u011fi teorisinin kazand\u0131r\u0131larak g\u00fcncel uygulamalara adapte edilmesi bu dersin hedefleri aras\u0131ndad\u0131r.<\/p>\n

Dersin \u00c7\u0131kt\u0131lar\u0131<\/strong><\/p>\n

Bu dersin sonunda kat\u0131l\u0131mc\u0131lar\u0131n a\u015fa\u011f\u0131daki hedeflere ula\u015fmas\u0131 beklenmektedir:<\/p>\n

    \n
  1. Klasik veri madencili\u011fi problemlerini anlayabiliyor olmak ve verilen bir problemin veri madencili\u011fi d\u00fcnyas\u0131ndaki kar\u015f\u0131l\u0131\u011f\u0131n\u0131 anlayabiliyor olmak.<\/li>\n
  2. Veri madencili\u011fi d\u00fcnyas\u0131ndaki klasik problemleri \u00e7\u00f6zebilecek yeterlilikte olmak.<\/li>\n
  3. Veri madencili\u011finde kullan\u0131lan temel y\u00f6ntemleri anlayabiliyor, ger\u00e7ek problemlere uygulayabiliyor ve alternatif y\u00f6ntemler aras\u0131ndan do\u011fru y\u00f6ntemi se\u00e7ebiliyor olmak.<\/li>\n
  4. B\u00fcy\u00fck veri ve b\u00fcy\u00fck veri d\u00fcnyas\u0131na ait problemleri tan\u0131mlayabiliyor olmak<\/li>\n
  5. B\u00fcy\u00fck veri d\u00fcnyas\u0131ndaki veri madencili\u011fi uygulamalar\u0131n\u0131 anlayabiliyor ve mevcut \u00e7\u00f6z\u00fcm y\u00f6ntemlerini biliyor olmak.<\/li>\n
  6. En az bir adet veri madencili\u011fi arac\u0131n\u0131 kullanabiliyor olmak ve ger\u00e7ek veriler \u00fczerinde derste anlat\u0131lan teorik y\u00f6ntemleri uygulayabiliyor olmak.<\/li>\n
  7. Veri madencili\u011fi \u00e7al\u0131\u015fmalar\u0131n\u0131n istatistiksel arka plan\u0131n\u0131 anlayabiliyor olmak ve istatistik ile veri madencili\u011fi \u00e7al\u0131\u015fmalar\u0131n\u0131 ilintilendirebiliyor olmak.<\/li>\n<\/ol>\n

    Introduction:<\/strong> Data mining studies, parallel to the increasing trend of Internet technologies and all other data sources has an important impact on the computer science world. One major aim of data mining studies is processing the data and reaching knowledge level results from the data. Especially processing data in big volumes and with high speed, great variety and with trust problems are major problems of today\u2019s data mining problems. Also the data mining theory and classical data mining approaches will be covered during the class and the practice of theoretical back ground on real world examples will be covered.<\/p>\n

    COURSE OBJECTIVES <\/strong><\/p>\n

      \n
    1. Understanding of data mining problems and ability to find a solution in data mining world for a real life problem<\/li>\n
    2. Ability to solve generic data mining problems<\/li>\n
    3. Understanding basic techniques in data mining world and ability to adapt these solutions into real world problems. Ability to select the correct solution methods among alternatives.<\/li>\n
    4. Ability to define the problems of big data<\/li>\n
    5. Understanding the data mining applications on big data problems and knowledge of current techniques<\/li>\n
    6. Ability to use at least one data mining suite.<\/li>\n
    7. Understanding the statistical background of data mining studies and relating the statistical methods with data mining.<\/li>\n<\/ol>\n

      Ders Kitab\u0131 (REQUIRED COURSE MATERIALS )<\/strong><\/p>\n

        \n
      1. TEXTBOOK:
        \nIan H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)<\/em>, Morgan Kaufmann, 2005, ISBN:\u00a00-12-088407-0.<\/li>\n
      2. TEXTBOOK 2:<\/li>\n<\/ol>\n

        Mining of Massive Datasets, Jure Leskovec<\/a>, Anand Rajaraman<\/a>, Jeff Ullman<\/a>, http:\/\/infolab.stanford.edu\/~ullman\/mmds\/book.pdf<\/p>\n

        Derste Kullan\u0131lacak Yaz\u0131l\u0131mlar (Softwares Required)<\/strong><\/p>\n