Instructor: Mustafa Ozdal (EA420)
TA: Huseyin Gokhan Akcay (EA-427)
Tutor: Orcun Gumus
Textbook: A. Rajaraman and J. D. Ullman, Mining of
Massive Datasets, Cambridge University Press, 2011. Online free version
available at: http://www.mmds.org
Schedule: Tue. 15:40-16:30, Fri. 13:40-15:30 (EA-Z03)
Spare Hour: Tue. 16:40-17:30 (EA-Z03)
Syllabus: syllabus.pdf
Announcements:
·
24/11/2015: You should
have received the project presentation schedule by now. If not, contact the TA
or tutor immediately.
·
26/10/2015: Midterm topics will cover lectures 1-6 (WebAdvertising will not be included). You will be allowed
to bring a single-sided A4-size cheatsheet with you.
You are supposed to turn in your cheatsheet with your
exam.
·
5/10/2015: Midterm will be on 13/11/2015 during
lecture hours (13:30-15:30). The classrooms reserved for the midterm are EA-Z03,
EB-102, and EB-103.
·
5/10/2015: You can find the project description
document here. Note that you are supposed to send the
TA and tutor your project proposals by October 16. See the pdf file for
details.
·
18/09/2015: No class on 22/09/2015. Instead, we will
use the back-up hour on 29/09/2015.
·
10/09/2015: Students are expected to check this page
regularly for important announcements.
Course Project
The project description with the upcoming deadlines
can be found here.
Lectures
Note: Some lecture notes provided below
contain slides from the course textbook. Some of these slides have been modified for the purpose of
this class. The original slides from the textbook can be accessed here.
Lecture 1: PageRank Formulation and Algorithm (slides: ppt, pdf; reading material: Chapter 5)
Lecture 2: PageRank Extensions (slides: ppt, pdf; reading material: Chapter 5)
Lecture 3: Shingling, Min-Hashing, and LSH (slides: ppt, pdf;
reading material: Chapter
3)
Lecture 4: LSH Applications (slides: ppt, pdf; reading material: Chapter 3)
Lecture 5: MapReduce Model and Examples (slides: ppt, pdf; reading material: Chapter 2)
Lecture 6: MapReduce Complexity Analysis and Improved Algorithms (slides: ppt, pdf; reading material: Chapter 2)
Lecture 7: Web Advertising (slides: ppt, pdf;
reading material: Chapter
8)
Lecture 8: Recommendation Systems: Content-Based and Collaborative Filtering
(slides: ppt, pdf; reading material: Chapter 9)
Lecture 9: Recommendation Systems: Latent Factor Models and Netflix Challenge
(slides: ppt, pdf; reading
material: Chapter 9)