Fiches de cours 2016-2017

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Internet analytics

COM-308

Enseignant(s) :

Grossglauser Matthias

Langue:

English

Summary

Internet analytics is the collection, modeling, and analysis of user data in large-scale online services, such as social networking, e-commerce, search, and advertisement. This class explores a number of the key functions of such online services that have become ubiquitous over the past decade.

Content

The class seeks a balance between foundational but relatively basic material in algorithms, statistics, graph theory and related fields, with real-world applications inspired by the current practice of internet and cloud services.

Specifically, we look at social & information networks, recommender systems, clustering and community detection, search/retrieval/topic models, dimensionality reduction, stream computing, and online ad auctions. Together, these provide a good coverage of the main uses for data mining and analytics applications in social networking, e-commerce, social media, etc.

The course is combination of theoretical materials and weekly laboratory sessions, where we explore several large-scale datasets from the real world. For this, you will work with a dedicated infrastructure based on Hadoop & Apache Spark.

 

Keywords

data mining; machine learning; social networking; map-reduce; hadoop; recommender systems; clustering; community detection; topic models; information retrieval; stream computing; ad auctions

Learning Prerequisites

Required courses

Stochastic models in communication (COM-300)

Recommended courses

Basic linear algebra

Algorithms & data structures

 

Important concepts to start the course

Graphs; linear algebra; Markov chains; Java

Learning Outcomes

By the end of the course, the student must be able to:

Teaching methods

Ex cathedra + homeworks + lab sessions

Expected student activities

Lectures with associated homeworks explore the basic models and fundamental concepts. The labs are designed to explore very practical questions based on a number of large-scale real-world datasets we have curated for the class. The labs draw on knowledge acquired in the lectures, but are hands-on and self-contained.

Assessment methods

Project 20%, midterm 30%, final exam 50%

Resources

Bibliography

C. Bishop, Pattern Recognition and MachineLearning, Springer, 2006

A. Rajaraman, J. D. Ullman: Mining of Massive Datasets, 2012

M. Chiang: Networked Life, Cambridge, Cambridge, 2012

D. Easley, J. Kleinberg: Networks, Crowds, and Markets, Cambridge, 2010

Ch. D. Manning, P. Raghavan, H. Schütze: Introduction to Information Retrieval, Cambridge, 2008

M.E.J. Newman: Networks: An Introduction, Oxford, 2010

 

Websites

Dans les plans d'études

Semaine de référence

 LuMaMeJeVe
8-9  BC01
BC07-08
  
9-10    
10-11  BC01
BC07-08
  
11-12     
12-13     
13-14     
14-15     
15-16     
16-17INM200    
17-18    
18-19     
19-20     
20-21     
21-22     
 
      Cours
      Exercice, TP
      Projet, autre

légende

  • Semestre d'automne
  • Session d'hiver
  • Semestre de printemps
  • Session d'été
  • Cours en français
  • Cours en anglais
  • Cours en allemand