Internet analytics
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
COM-300 Modèles stochastiques pour les communications
Recommended courses
Basic linear algebra
Algorithms & data structures
Important concepts to start the course
Graphs; linear algebra; Markov chains; Python
Learning Outcomes
By the end of the course, the student must be able to:
- Explore real-world data from online services
- Develop framework 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
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 35%, final exam 65%
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
Ressources en bibliothèque
- Networks, Crowds, and Markets / Easley
- Pattern Recognition and Machine Learning / Bishop
- Introduction to Information Retrieval / Manning
- Networked Life / Chiang
- Mining of Massive Dataset / Leskovec
Moodle Link
In the programs
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Internet analytics
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Internet analytics
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Internet analytics
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Internet analytics
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
- Type: optional