Informações sobre o curso
4.4
399 classificações
87 avaliações
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Horas para completar

Aprox. 20 horas para completar

Sugerido: 6 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês

Habilidades que você terá

Information Retrieval (IR)Document RetrievalMachine LearningRecommender Systems
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Horas para completar

Aprox. 20 horas para completar

Sugerido: 6 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
Horas para completar
2 horas para concluir

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 leituras, 2 testes
Video2 videos
Course Introduction Video11min
Reading6 leituras
Welcome to Text Retrieval and Search Engines!10min
Syllabus10min
About the Discussion Forums10min
Updating your Profile10min
Social Media10min
Course Errata10min
Quiz2 exercícios práticos
Orientation Quiz15min
Pre-Quiz30min
Horas para completar
4 horas para concluir

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 leitura, 2 testes
Video6 videos
Lesson 1.2: Text Access9min
Lesson 1.3: Text Retrieval Problem26min
Lesson 1.4: Overview of Text Retrieval Methods10min
Lesson 1.5: Vector Space Model - Basic Idea9min
Lesson 1.6: Vector Space Retrieval Model - Simplest Instantiation17min
Reading1 leituras
Week 1 Overview10min
Quiz2 exercícios práticos
Week 1 Practice Quizmin
Week 1 Quizmin
Semana
2
Horas para completar
4 horas para concluir

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 leitura, 2 testes
Video6 videos
Lesson 2.2: TF Transformation9min
Lesson 2.3: Doc Length Normalization18min
Lesson 2.4: Implementation of TR Systems21min
Lesson 2.5: System Implementation - Inverted Index Construction18min
Lesson 2.6: System Implementation - Fast Search17min
Reading1 leituras
Week 2 Overview10min
Quiz2 exercícios práticos
Week 2 Practice Quizmin
Week 2 Quizmin
Semana
3
Horas para completar
7 horas para concluir

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 leituras, 3 testes
Video6 videos
Lesson 3.2: Evaluation of TR Systems - Basic Measures12min
Lesson 3.3: Evaluation of TR Systems - Evaluating Ranked Lists - Part 115min
Lesson 3.4: Evaluation of TR Systems - Evaluating Ranked Lists - Part 210min
Lesson 3.5: Evaluation of TR Systems - Multi-Level Judgements10min
Lesson 3.6: Evaluation of TR Systems - Practical Issues15min
Reading2 leituras
Week 3 Overview10min
Programming Assignments Overview10min
Quiz2 exercícios práticos
Week 3 Practice Quizmin
Week 3 Quizmin
Semana
4
Horas para completar
4 horas para concluir

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 leitura, 2 testes
Video7 videos
Lesson 4.2: Statistical Language Model17min
Lesson 4.3: Query Likelihood Retrieval Function12min
Lesson 4.4: Statistical Language Model - Part 112min
Lesson 4.5: Statistical Language Model - Part 29min
Lesson 4.6: Smoothing Methods - Part 19min
Lesson 4.7: Smoothing Methods - Part 213min
Reading1 leituras
Week 4 Overview10min
Quiz2 exercícios práticos
Week 4 Practice Quizmin
Week 4 Quizmin
4.4
87 avaliaçõesChevron Right
Benefício de carreira

83%

consegui um benefício significativo de carreira com este curso
Promoção de carreira

33%

recebi um aumento ou promoção

Melhores avaliações

por 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.

por 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)

Instrutores

Avatar

ChengXiang Zhai

Professor
Department of Computer Science
Graduation Cap

Start working towards your Master's degree

This curso 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.

Sobre 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. ...

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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....
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