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Lecture 3 - CSCI494 - Anatomy of Search Engine (Coding a Basic Crawler)

January 26th, 2012 admin No comments

CSCI494 Lect. 3 Jan. 25 2012 (Slides)

Last lecture (1/25/12) we discussed the elements of a search engine including crawlers, spiders, indexers, repositories, lexicons, ranking modules, and query processors. We also talked about fetching URLs and reviewed initial code for a crawler for assignment 2.

Here are the slides:
Anatomy of A Search Engine. Assignment 2 Building a Basic Crawler.

Next week we will derive “The Google Matrix” formula.

Computer Science 494 Search Engines & Social Networks MSU- Spring 2012

January 12th, 2012 admin No comments

I was invited by MSU to teach CSCI 494-01 as an adjunct professor. The course is a senior-level course on search engines & social networks for computer science majors. Here is a description of the course and the topics to be covered:

Syllabus- CSCI 494 01 Spring 2012 - Search Engines and Social Networks

Description:
This course will cover important topics related to search engines, social networks, information retrieval, and data science. Students will study papers, patents, and algorithms written by search engineers and computer scientists. At the end of the course students will understand the algorithms and technology behind modern search engines and social networks.

Prerequisites
Calculus, completion of at least one course in a programming language (Java/C++), and HTML/CSS.

Date Lecture Topic
1.11 Introduction & Overview
1.18 History of Search
1.25 Overview of Search Marketing
2.01 Organic Search: Search Algorithms
2.08 Information Retrieval
2.15 Patents/Papers: Organic Search
2.22 Papers: Organic Search
2.29 Paid Search Algorithms
3.07 Social Networks & Algorithms
3.14 Spring Break – No Class
3.21 Social Networks & Algorithms
Paper titles for presentation due (Groups of three)
3.28 Analytics, Metrics, and Data Science
4.04 Semantic Web Technology (TBD)
4.11 Presentations 20 min TBD
4.18 Presentations 20 min TBD
4.25 Presentations 20 min TBD

Textbook
• Required papers and patents to be handed out during lecture.

Course Outcomes
• Understand algorithms behind modern search engines.
• Understand paid search marketing algorithms.
• Understand the history of search engines.
• Understand important patents related to search engines.
• Be prepared for basic interview questions from companies that develop search engines and/or social networks.
• Discover potential Thesis Topics for Graduate School

Grading
25% of a student’s grade will come from attendance of regular lectures and attendance of the final presentations and 75% will come from the student’s final presentations and papers.
Final presentations will be graded as follows:
50% on the groups 20 minute final presentation
50% on the individual’s 1-2 page paper

At the end of the semester, grades will be determined based on your class average as follows:
• 93+: A
• 90+: A-
• 87+: B+
• 83+: B
• 80+: B-
• 77+: C+
• 73+: C
• 70+: C-
• 67+: D+
• 63: D
• 60: D-

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