CP421B - Data Mining (Winter 2027)

Wilfrid Laurier University • Waterloo, ON

2d • Onsite

About The Position

The course is aimed at an entry level study of information retrieval and data mining techniques. It is about how to find relevant information and subsequently extract meaningful patterns out of it. While the basic theories and mathematical models of information retrieval and data mining are covered, the course is primarily focused on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations. At the end of the course student are expected to understand the following: 1. The common algorithms and techniques for information retrieval (document indexing and retrieval, query processing, etc). 2. The quantitative evaluation methods for the IR systems and data mining techniques. 3. The popular probabilistic retrieval methods and ranking principles. 4. The techniques and algorithms existing in practical retrieval and data mining systems such as those in web search engines and recommender systems. 5. The challenges and existing techniques for the emerging topics of MapReduce, portfolio retrieval and online advertising.

Requirements

• Master’s degree
• Computer Science or related field
• Demonstrated expertise in the subject field
• Recent scholarly activity related to the course content
• CV
• Candidate Application Form (CAF)

Nice To Haves

• PhD would be an asset

Responsibilities

• Teach entry-level study of information retrieval and data mining techniques.
• Cover basic theories and mathematical models of information retrieval and data mining.
• Focus on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, and their performance evaluations.
• Explain common algorithms and techniques for information retrieval (document indexing and retrieval, query processing, etc).
• Discuss quantitative evaluation methods for IR systems and data mining techniques.
• Explain popular probabilistic retrieval methods and ranking principles.
• Detail techniques and algorithms in practical retrieval and data mining systems (e.g., web search engines, recommender systems).
• Cover challenges and techniques for emerging topics like MapReduce, portfolio retrieval, and online advertising.

What This Job Offers

• Job Type: Part-time
• Career Level: Entry Level