Instructor(s):

András Benczúr
Weeks
1-7
Contact hours
2x2 hours
Credit
2 credits

Short Description of the Course:
Data Scientist is called "the sexiest job of the Century" by Harvard Business Review. In the first part of the course, we learn the basics of understanding data and predicting its unknown properties.

We give a general introduction to data analysis, modeling, and algorithms of data mining.The course provides a good base for its follow-up course, Data Mining Applications.Lectures are supplemented by computer exercises and student projects in small teams.

Aim of the Course:
The aim of the course is to provide a basic but comprehensive introduction to data mining. By the end of the course students will be able to choose the right algorithms for data science problems to build, implement and evaluate data mining models.

Prerequisites:
The course requires basic knowledge in calculus, probability theory, and linear algebra. Knowledge of graphs and basic algorithms is an advantage. Basic programming skills are also required.

Detailed Program and Class Schedule:

  • Motivations for data mining. Examples of application domains.
  • Analyzing data: preparation and exploration.
  • Models and algorithms for classification.
  • Introduction to the IPython Notebook and python based data mining software packages. Classification with scikit-learn.
  • Basics of classification. Concepts of training and prediction. Measuring quality and comparison of classification models.
  • Type of variables, measuring similarity and distances. The k-nearest neighbor classifier.
  • Decision trees, naive Bayes. The concept of model over and underfitting. Midterm test.
  • Basics of cluster analysis. Partitioning clustering algorithms, k-means, k-medoids.
  • Hierarchical clustering algorithms.
  • Introduction to frequent itemset mining. Applications for finding association rules. Level-wise algorithms, APRIORI.
  • Final test.

Method of instruction:
Handouts, presentations, IPython Notebooks, relevant research papers, web page, course mailing list and Wiki. Weekly regular office hour for consultations..

Textbooks:
Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Addison-Wesley, 2006.

Jure Leskovec, Anand Rajaraman, Jeff Ullman: Mining of Massive Datasets

http://www.mmds.org/.

Instructors' bio:

András Benczúr (born 1969) is a senior researcher of the Computer Science and Automation Institute of the Hungarian Academy of Science (MTA SZTAKI). He is co-founder of the Data Mining and Web Search Group and head of the Informatics Laboratory. He has been teaching Algorithms, and Web Information Retrieval at Eötvös Loránd University and Statistics at Central European University (CEU), Budapest. He received his Ph.D. degree at MIT, US in 1997. His primary research areas are information retrieval, data mining and algorithms. He has been awarded the “Young Researcher Award” and the “Béla Gyires Award” of the Hungarian Academy of Sciences. He won a “Yahoo! Faculty Research Grant” in 2006. Benczúr’s group won 1st place at the KDD Cup of the ACM in 1997. He is the author or co-author of more than 30 refereed research papers with over 200 citations. He has served as coordinator and/or principal researcher of several national and international information retrieval and data mining projects.