About this Course
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Course 2 of 6 in the

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Flexible deadlines

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Approx. 19 hours to complete

English

Subtitles: English

Skills you will gain

Information Retrieval (IR)Document RetrievalMachine LearningRecommender Systems

Course 2 of 6 in the

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 19 hours to complete

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Orientation

2 videos (Total 15 min), 6 readings, 2 quizzes
2 videos
Course Introduction Video11m
6 readings
Welcome to Text Retrieval and Search Engines!10m
Syllabus10m
About the Discussion Forums10m
Updating your Profile10m
Social Media10m
Course Errata10m
2 practice exercises
Orientation Quiz15m
Pre-Quiz30m
4 hours to complete

Week 1

6 videos (Total 94 min), 1 reading, 2 quizzes
6 videos
Lesson 1.2: Text Access9m
Lesson 1.3: Text Retrieval Problem26m
Lesson 1.4: Overview of Text Retrieval Methods10m
Lesson 1.5: Vector Space Model - Basic Idea9m
Lesson 1.6: Vector Space Retrieval Model - Simplest Instantiation17m
1 reading
Week 1 Overview10m
2 practice exercises
Week 1 Practice Quiz1h
Week 1 Quiz1h
Week
2
4 hours to complete

Week 2

6 videos (Total 102 min), 1 reading, 2 quizzes
6 videos
Lesson 2.2: TF Transformation9m
Lesson 2.3: Doc Length Normalization18m
Lesson 2.4: Implementation of TR Systems21m
Lesson 2.5: System Implementation - Inverted Index Construction18m
Lesson 2.6: System Implementation - Fast Search17m
1 reading
Week 2 Overview10m
2 practice exercises
Week 2 Practice Quiz1h
Week 2 Quiz1h
Week
3
7 hours to complete

Week 3

6 videos (Total 75 min), 2 readings, 3 quizzes
6 videos
Lesson 3.2: Evaluation of TR Systems - Basic Measures12m
Lesson 3.3: Evaluation of TR Systems - Evaluating Ranked Lists - Part 115m
Lesson 3.4: Evaluation of TR Systems - Evaluating Ranked Lists - Part 210m
Lesson 3.5: Evaluation of TR Systems - Multi-Level Judgements10m
Lesson 3.6: Evaluation of TR Systems - Practical Issues15m
2 readings
Week 3 Overview10m
Programming Assignments Overview10m
2 practice exercises
Week 3 Practice Quiz1h
Week 3 Quiz1h
Week
4
4 hours to complete

Week 4

7 videos (Total 88 min), 1 reading, 2 quizzes
7 videos
Lesson 4.2: Statistical Language Model17m
Lesson 4.3: Query Likelihood Retrieval Function12m
Lesson 4.4: Statistical Language Model - Part 112m
Lesson 4.5: Statistical Language Model - Part 29m
Lesson 4.6: Smoothing Methods - Part 19m
Lesson 4.7: Smoothing Methods - Part 213m
1 reading
Week 4 Overview10m
2 practice exercises
Week 4 Practice Quiz1h
Week 4 Quiz1h
4.4
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Top reviews from Text Retrieval and Search Engines

By JHSep 21st 2016

Great course for those trying to understand how ro analyse and process text data. It has the right amount of tools to help you understand the basics of information retrieval and search engines.

By PMAug 29th 2016

A great overview of text retrieval methods. Good coverage of search engines. A longer course will cover search engine better (remember this is a 6 weeker)

Instructor

Avatar

ChengXiang Zhai

Professor
Department of Computer Science

Start working towards your Master's degree

This course is part of the 100% online Master in Computer Science from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

About University of Illinois at Urbana-Champaign

The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs. ...

About the Data Mining Specialization

The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization....
Data Mining

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