COMPSCI 646 is a graduate-level course in Information Retrieval, the science and engineering of indexing, organizing, searching, and making sense of unstructured or mostly unstructured information, particularly text. The class focuses primarily on the underlying models used for effective search and organization, but includes some discussion of efficiency concerns. The course also covers current research problems and methodologies in the field of Information Retrieval.
- Proficiency in Python and/or Java
- Basic knowledge of probability, statistics, and information theory
- Foundations of applied machine learning and deep learning
- [WBC] W. Bruce Croft, Donald Metzler, and Trevor Strohman. Search Engines: Information Retrieval in Practice. Pearson Education, 2009.
- [CDM] Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. Introduction to Information Retrieval. Cambridge University Press, 2008.
- Assignments (3×10%)
- IR Talk Summaries (10%)
- Midterm exam (30%): November 9, 2023 at 7 PM. Location: Herter 227.
- Final project (30%)
|2||IR and ML Basics||Thu 9/7|
|4||IR Evaluation Methodologies, Metrics, and User Models||Thu 9/14||
|6||Indexing, Vector Space Models & LSI||Thu 9/21||
|7||Probabilistic Retrieval Models and BM25||Tue 9/26|
|8||Language Modeling||Thu 9/28||
|10||Query Expansion & Relevance Feedback||Thu 10/5|
|Monday Schedule||Tue 10/10|
|11||Learning to Rank||Thu 10/12|
Distributed Representation Learning for Text
|13||Contextual Representations and Large Language Models||Thu 10/19|
|14||Neural Ranking Models||Tue 10/24|
|16||Implicit Feedback, Biases, and Click Models||Tue 10/31|
|17||Link Analysis & MapReduce||Thu 11/2|
|18||Novelty and Diversity||Tue 11/7|
|19||Information Filtering and Recommendation||Thu 11/9|
|20||IR Applications: CLIR, Personalization, Entity Retrieval||Tue 11/14|
|22||User Study, Crowdsourcing, and Query Log Analysis||Tue 11/21|
|No Class: Thanksgiving||Thu 11/23|
|23||Retrieval-Enhanced Machine Learning: QA, Fact Verification, and Beyond||Tue 11/28|
|25||Context-Aware & Conversational Search||Tue 12/5|
|26||Current IR Research||Thu 12/7|
Each student has a total of 5 late days without penalty. You can use up to 3 late days per assignment, talk summary, or project milestone excluding the project’s final report. Once you use all your late submission credits, you will lose 20% (absolute) of the homework points per day.
In case of multiple submissions of an assignment, only the last one will be taken into account for the number of late days.
Collaboration and Help
You may discuss the ideas behind assignments with others. You may ask for help understanding class and IR concepts. You may study with friends. However…
The work that you submit must be your own. It may not be copied from the web, from another student in the class, or from anyone else. If you stumble upon and use a solution from the textbook or from class, you are expected to acknowledge the source of the work.
Your assignment submissions must be your own work and not in collaboration with anyone. Your project work must be your own work and not a copy of someone else’s work.