• 1.10.15: This course is meant for the 3rd year bachelor Web Science (both major and Bachelor college), Software Science and Web Technology programs.
• 1.10.15: Course and examination information and registration on OWInfo.

• Send all correspondence to 2IID0.Teachers@gmail.com with a meaningful subject; it is fine to start with Hi, Hello or Dear FirstName.
• Please do not send requests to our personal e-mails. There is also no need cc personal e-mails addresses of the teachers.
• We will try to answer all your requests as soon as possible. However, if you have not received a reply within 3 working days please do not hesitate to resend your request.

• option 1: please, do not hesitate to approach the teachers during the lecture breaks and during the instructions in Flux 0.01;
• option 2: on Mondays 10.00 - 12.00 we have office hours in MF7.099 dedicated for the educational activities;
• option 3: if you cannot make it during the lecture breaks or our dedicated office hours, please send a meeting request to 2IID0.Teachers@gmail.com indicating your availability for the corresponding period;
  

• 8 weeks x 2 times per week lectures
• 8 weeks x 1 time per week instructions (all students are in the same instructions group)
• Self-study of the literature
• 4 Homeworks; done in groups of 2-3. For every assignment - you need to make a new composition of groups.
• Question answering sessions
• Written exam.
• Optional bonus assignment. More on this during Week1.

Final grade:

• 50% homeworks (2IID2) and 50% written exam (2IID1).
• You have to get at least 5.5 as a grand average to pass the course. An additional constraint imposed by the Bachelor college is that you need to score at least 5.0 for the exam and at least 5.0 for the homeworks to pass the course.

• There is no single text book that covers the topics you will study in this course. However, there are several good book chapters that cover some of the topics. These chapters are available online:
  • MMDS: Mining of Massive Datasets (by Rajaraman, Ullman, Leskovec) accessible online from here.
  • IDM: Introduction to Data Mining book (by Tan, Steinbach, Kumar), selected chapters accessible online from here.
  • DMT: Data Mining: The Textbook (by Aggarwal), all chapters accessible online from here.
  • NCM: Networks, Crowds, and Markets: Reasoning About a Highly Connected World (by Easley and Kleinberg), accessible online from here.
  For each covered topic the corresponding book chapter(s) or other reading will be suggested.
• Lecture slides, reading materials, homework description and guidelines will be available via Sakai Learning Management System. Please register using your TU/e login, and join 2IID0.
  
• Submission of the homeworks will be done via the course e-mail.

Course Syllabus:

Please note that this schedule is indicative and changes may be possible as the course progresses.

Colour agenda:

Handouts and course materials will be available with Sakai or other Learning Management System.

Remarks:

• MMDS: Mining of Massive Datasets (by Rajaraman, Ullman, Leskovec) accessible online from here.
• IDM: Introduction to Data Mining book (by Tan, Steinbach, Kumar), chapters accessible online from here.
• DMT: Data Mining: The Textbook (by Aggarwal), all chapters accessible online from here.
• NCM: Networks, Crowds, and Markets: Reasoning About a Highly Connected World (by Easley and Kleinberg), accessible online from here.
• Please notice that this schedule is indicative and some changes may be still possible.
• Last update: 17 Nov 2015; if you notice any outdated or wrong information on this webpage, please e-mail to 2IID0.Teachers@gmail.com