DM4032
Datamining
Course Materials
Grading:

            Projects/Programming Assignments:    Quizzes, Class Participation and Attendance:
            Midterms:                   Midterm Date:                                Final:

Text Books: 
    1- Data Mining Concepts and Techniques, Jiawei Han, Micheline Kamber and Jian Pei, 2012.
    2- Matrix Methods in Data Mining and Pattern Recognition, Lars Eld´en, 2007.
    3- A Programmer’s Guide to Data Mining, Ron Zacharski.
  •     Course Outlines
       Libraris:
            Numpy: Numpy.pdf
             Pandas: Pandas.pdf
   
       Introduction:
       KDD vs Data Mining: link
       Getting to Know Your Data: TypesOfData, (link: chapter 2)        
       Data Preprocessing: (link: chapter 3)
       Data Warehousing:  (link: chapter 4)      
       Mining Frequent Patterns, Associations, and Correlations: (link: chapter 6)
       Regression: Regression
       Classification: Classification, Metrics, Naive_Bayes (link: chapters 8 and 9)
       Clustering: Part1, Part2, (link: chapter 10)
       Recommendation System: (link: chapters 2 and 3 ), Matrix Factorization
      
       
       Final Project 
Course Information
Lecture Notes
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