Study group for reading the text of "Mining of massive datasets" from Stanford.(Selected chapters)
Hi, I'm looking to learn together in a group, we are going to cover the chapters 3,6,7,9 from "Mining of Massive Datasets by Anand Rajaraman and Ullman" to help get a grasp of subject aiming at **recommender systems** however the study can also be looked as an intro ML. The chapters are listed below
Chapter 3 Finding Similar Items
Chapter 6 Frequent Itemsets
Chapter 7 Clustering
Chapter 9 Recommendation Systems
I'm planning to finish the stipulated work in 2 weeks. The total reading material consists of 152 pages and requirements for reading are
Basics in Set Theory
Basics in Probability & Statistics
Basic Trigonometry and Geometry
No programming experience required.(We are going to get the concept now i'll start another session particularly aiming at implementing the algorithms learned now in Python)
We are going to implement a well planned schedule for studying the text and solving the exercises in the text, this requires a lot of effort i hope which would surely get you prepared for to tackle challenges.
We'll regularly keep track of the group progress through discussions on a google group and hangouts on G+ for further discussion on the learnt topics and solving the exercises.
Book is available for free download at http://infolab.stanford.edu/~ullman/mmds.html
The Google Group is https://groups.google.com/forum/#!forum/redditmmds),Everyone who's interested is welcomed to join the group.
The actual Reddit post http://www.reddit.com/r/CollaborateCode/comments/1lwiec/lfgltlstudy_group_for_reading_the_text_of_mining/
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