information retrieval homework

CS 276 / LING 286: Information Retrieval and Web Search

Course Description

• Efficient text indexing
• Boolean and vector-space retrieval models
• Evaluation and interface issues
• IR techniques for the web, including crawling, link-based algorithms, and metadata usage
• Document clustering and classification
• Traditional and machine learning-based ranking approaches

Course Instructors

Teaching Assistants

Class time & location

Office hours, grading policy, grading & course policies, assignment details, final project details, required textbook, other useful references.

• (MG) Managing Gigabytes , by I. Witten, A. Moffat, and T. Bell.
• (IRAH) Information Retrieval: Algorithms and Heuristics , by D. Grossman and O. Frieder.
• (MIR) Modern Information Retrieval , by R. Baeza-Yates and B. Ribeiro-Neto.
• (FSNLP) Foundations of Statistical Natural Language Processing , by C. Manning and H. Schütze.
• (SE) Search Engines: Information Retrieval in Practice , by B. Croft, D. Metzler, and T. Strohman.
• (IRIE) Information Retrieval: Implementing and Evaluating Search Engines , by S. Büttcher, C. Clarke, and G. Cormack.

Prerequisites

• Core programming and algorithm skills CS 107, CS 161, and ideally other courses in the "core" for CS majors provide good preparation. Note that we will be using bitwise operations in several labs and assignments, so it's a good idea to brush up on these concepts and their syntax if you're rusty on low-level data manipulation.
• Basic probability and statistics You should have a good grasp of the fundamentals of probability distributions and basic statistical calculations (mean, standard deviation, etc.) at the level of a course like CS 109.
• Proficiency in Python All class assignments this year will be in Python.
• Proficiency in Java All class assignments this year will be in Java. The links and other information in this document may be useful for those who would benefit from a tutorial or refresher on Java and the Ant build tool.
• Convex optimization You may find some of the optimization tricks more intuitive with this background.
• Knowledge of convolutional neural networks (CS231n) The first problem set will probably be easier for you. We cannot assume you took this class so there will be ~3 lectures that overlap in content. You can use that time to dive deeper into some aspects.

Programming Tutorials

• Python for programmers While Python is wildly popular, this class was traditionally taught with programming assignments in Java. Here are a few Python Tutorials for programmers .
• Bit Manipulation in Python Bit Manipulation in Python . Although you might not need any of it, it might come in handy to brush up your bit manipulation skills.
• Get started guide
• Official documentation

Course Schedule