About this Course
4.4
392 ratings
85 reviews
Specialization

Course 2 of 6 in the

100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Hours to complete

Approx. 20 hours to complete

Suggested: 6 hours/week...
Available languages

English

Subtitles: English...

Skills you will gain

Information Retrieval (IR)Document RetrievalMachine LearningRecommender Systems
Specialization

Course 2 of 6 in the

100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Hours to complete

Approx. 20 hours to complete

Suggested: 6 hours/week...
Available languages

English

Subtitles: English...

Syllabus - What you will learn from this course

Week
1
Hours to complete
2 hours to complete

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....
Reading
2 videos (Total 15 min), 6 readings, 2 quizzes
Video2 videos
Course Introduction Video11m
Reading6 readings
Welcome to Text Retrieval and Search Engines!10m
Syllabus10m
About the Discussion Forums10m
Updating your Profile10m
Social Media10m
Course Errata10m
Quiz2 practice exercises
Orientation Quiz15m
Pre-Quiz30m
Hours to complete
4 hours to complete

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. ...
Reading
6 videos (Total 94 min), 1 reading, 2 quizzes
Video6 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
Reading1 reading
Week 1 Overview10m
Quiz2 practice exercises
Week 1 Practice Quizm
Week 1 Quizm
Week
2
Hours to complete
4 hours to complete

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. ...
Reading
6 videos (Total 102 min), 1 reading, 2 quizzes
Video6 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
Reading1 reading
Week 2 Overview10m
Quiz2 practice exercises
Week 2 Practice Quizm
Week 2 Quizm
Week
3
Hours to complete
7 hours to complete

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....
Reading
6 videos (Total 75 min), 2 readings, 3 quizzes
Video6 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
Reading2 readings
Week 3 Overview10m
Programming Assignments Overview10m
Quiz2 practice exercises
Week 3 Practice Quizm
Week 3 Quizm
Week
4
Hours to complete
4 hours to complete

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. ...
Reading
7 videos (Total 88 min), 1 reading, 2 quizzes
Video7 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
Reading1 reading
Week 4 Overview10m
Quiz2 practice exercises
Week 4 Practice Quizm
Week 4 Quizm
4.4
85 ReviewsChevron Right
Career Benefit

83%

got a tangible career benefit from this course
Career promotion

33%

got a pay increase or promotion

Top Reviews

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
Graduation Cap

Start working towards your Master's degree

This course is part of the 100% online Master of Computer Science in Data 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

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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