COM-308 / 5 crédits

Enseignant: Grossglauser Matthias

Langue: Anglais


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

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:

  • Explore real-world data from online services
  • Develop frameworks and models for typical data mining problems in online services
  • Analyze the efficiency and effectiveness of these models
  • data-mining and machine learning techniques to concrete real-world problems

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

  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Internet analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 1 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Internet analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 1 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Internet analytics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 1 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22