About this course: Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text. This course will cover search engine technologies, which play an important role in any data mining applications involving text data for two reasons. First, while the raw data may be large for any particular problem, it is often a relatively small subset of the data that are relevant, and a search engine is an essential tool for quickly discovering a small subset of relevant text data in a large text collection. Second, search engines are needed to help analysts interpret any patterns discovered in the data by allowing them to examine the relevant original text data to make sense of any discovered pattern. You will learn the basic concepts, principles, and the major techniques in text retrieval, which is the underlying science of search engines.
Created by: University of Illinois at Urbana-Champaign
Taught by: ChengXiang Zhai, Professor
Department of Computer Science
| Basic Info | Course 2 of 6 in the Data Mining Specialization |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
2 videos, 6 readings, 1 practice quiz
- Материал для самостоятельного изучения: Welcome to Text Retrieval and Search Engines!
- Материал для самостоятельного изучения: Syllabus
- Материал для самостоятельного изучения: About the Discussion Forums
- Материал для самостоятельного изучения: Updating your Profile
- Материал для самостоятельного изучения: Social Media
- Материал для самостоятельного изучения: Course Errata
- Video: Course Welcome Video
- Video: Course Introduction Video
- Тренировочный тест: Pre-Quiz
Graded: Orientation Quiz
Week 1
During this week's lessons, you will learn of natural language processing techniques, which are the foundation for all kinds of text-processing applications, the concept of a retrieval model, and the basic idea of the vector space model.
6 videos, 1 reading, 1 practice quiz
- Материал для самостоятельного изучения: Week 1 Overview
- Video: Lesson 1.1: Natural Language Content Analysis
- Video: Lesson 1.2: Text Access
- Video: Lesson 1.3: Text Retrieval Problem
- Video: Lesson 1.4: Overview of Text Retrieval Methods
- Video: Lesson 1.5: Vector Space Model - Basic Idea
- Video: Lesson 1.6: Vector Space Retrieval Model - Simplest Instantiation
- Тренировочный тест: Week 1 Practice Quiz
Graded: Week 1 Quiz
WEEK 2
Week 2
In this week's lessons, you will learn how the vector space model works in detail, the major heuristics used in designing a retrieval function for ranking documents with respect to a query, and how to implement an information retrieval system (i.e., a search engine), including how to build an inverted index and how to score documents quickly for a query.
6 videos, 1 reading, 1 practice quiz
- Материал для самостоятельного изучения: Week 2 Overview
- Video: Lesson 2.1: Vector Space Model - Improved Instantiation
- Video: Lesson 2.2: TF Transformation
- Video: Lesson 2.3: Doc Length Normalization
- Video: Lesson 2.4: Implementation of TR Systems
- Video: Lesson 2.5: System Implementation - Inverted Index Construction
- Video: Lesson 2.6: System Implementation - Fast Search
- Тренировочный тест: Week 2 Practice Quiz
Graded: Week 2 Quiz
WEEK 3
Week 3
In this week's lessons, you will learn how to evaluate an information retrieval system (a search engine), including the basic measures for evaluating a set of retrieved results and the major measures for evaluating a ranked list, including the average precision (AP) and the normalized discounted cumulative gain (nDCG), and practical issues in evaluation, including statistical significance testing and pooling.
6 videos, 2 readings, 1 practice quiz
- Материал для самостоятельного изучения: Week 3 Overview
- Video: Lesson 3.1: Evaluation of TR Systems
- Video: Lesson 3.2: Evaluation of TR Systems - Basic Measures
- Video: Lesson 3.3: Evaluation of TR Systems - Evaluating Ranked Lists - Part 1
- Video: Lesson 3.4: Evaluation of TR Systems - Evaluating Ranked Lists - Part 2
- Video: Lesson 3.5: Evaluation of TR Systems - Multi-Level Judgements
- Video: Lesson 3.6: Evaluation of TR Systems - Practical Issues
- Тренировочный тест: Week 3 Practice Quiz
- Материал для самостоятельного изучения: Programming Assignments Overview
Graded: Week 3 Quiz
Graded: Programming Assignment 1
WEEK 4
Week 4
In this week's lessons, you will learn probabilistic retrieval models and statistical language models, particularly the detail of the query likelihood retrieval function with two specific smoothing methods, and how the query likelihood retrieval function is connected with the retrieval heuristics used in the vector space model.
7 videos, 1 reading, 1 practice quiz
- Материал для самостоятельного изучения: Week 4 Overview
- Video: Lesson 4.1: Probabilistic Retrieval Model - Basic Idea
- Video: Lesson 4.2: Statistical Language Model
- Video: Lesson 4.3: Query Likelihood Retrieval Function
- Video: Lesson 4.4: Statistical Language Model - Part 1
- Video: Lesson 4.5: Statistical Language Model - Part 2
- Video: Lesson 4.6: Smoothing Methods - Part 1
- Video: Lesson 4.7: Smoothing Methods - Part 2
- Тренировочный тест: Week 4 Practice Quiz
Graded: Week 4 Quiz
WEEK 5
Week 5
In this week's lessons, you will learn feedback techniques in information retrieval, including the Rocchio feedback method for the vector space model, and a mixture model for feedback with language models. You will also learn how web search engines work, including web crawling, web indexing, and how links between web pages can be leveraged to score web pages.
8 videos, 1 reading, 1 practice quiz
- Материал для самостоятельного изучения: Week 5 Overview
- Video: Lesson 5.1: Feedback in Text Retrieval
- Video: Lesson 5.2: Feedback in Vector Space Model - Rocchio
- Video: Lesson 5.3: Feedback in Text Retrieval - Feedback in LM
- Video: Lesson 5.4: Web Search: Introduction & Web Crawler
- Video: Lesson 5.5: Web Indexing
- Video: Lesson 5.6: Link Analysis - Part 1
- Video: Lesson 5.7: Link Analysis - Part 2
- Video: Lesson 5.8: Link Analysis - Part 3
- Тренировочный тест: Week 5 Practice Quiz
Graded: Week 5 Quiz
WEEK 6
Week 6
In this week's lessons, you will learn how machine learning can be used to combine multiple scoring factors to optimize ranking of documents in web search (i.e., learning to rank), and learn techniques used in recommender systems (also called filtering systems), including content-based recommendation/filtering and collaborative filtering. You will also have a chance to review the entire course.
10 videos, 1 reading, 1 practice quiz
- Материал для самостоятельного изучения: Week 6 Overview
- Video: Lesson 6.1: Learning to Rank - Part 1
- Video: Lesson 6.2: Learning to Rank - Part 2
- Video: Lesson 6.3: Learning to Rank - Part 3
- Video: Lesson 6.4: Future of Web Search
- Video: Lesson 6.5: Recommender Systems: Content-Based Filtering - Part 1
- Video: Lesson 6.6: Recommender Systems: Content-Based Filtering - Part 2
- Video: Lesson 6.7: Recommender Systems: Collaborative Filtering - Part 1
- Video: Lesson 6.8: Recommender Systems: Collaborative Filtering - Part 2
- Video: Lesson 6.9: Recommender Systems: Collaborative Filtering - Part 3
- Video: Lesson 6.10: Course Summary
- Тренировочный тест: Week 6 Practice Quiz
Graded: Week 6 Quiz
Graded: Programming Assignment 2
FAQs
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Ratings and Reviews
Good theoretical course. Some exercises need to be fixed, as they count for approval and are misplaced.
very great course but , the programing language was C++ intead of Python :(
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For understanding evaluation of search ranking algorithms, and picking up the ideas behind Learning To Rank, this course is great. The only glitch is that some of the quiz questions ask about material from weeks not yet covered!