BSc: Information Retrieval - Revision history

M.petrishchev: /* Course grading range */

2023-09-16T09:46:17Z

Course grading range

← Older revision		Revision as of 09:46, 16 September 2023
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	! Grade !! Range !! Description of performance		! Grade !! Range !! Description of performance
	\|-		\|-
−	\| A. Excellent \|\| 84-100 \|\| -	+	\| A. Excellent \|\| 90-100 \|\| -
	\|-		\|-
−	\| B. Good \|\| 72-83 \|\| -	+	\| B. Good \|\| 75-89 \|\| -
	\|-		\|-
−	\| C. Satisfactory \|\| 60-71 \|\| -	+	\| C. Satisfactory \|\| 60-74 \|\| -
	\|-		\|-
	\| D. Poor \|\| 0-59 \|\| -		\| D. Poor \|\| 0-59 \|\| -

S.protasov: /* Open access resources */

2022-12-19T14:32:54Z

Open access resources

← Older revision		Revision as of 14:32, 19 December 2022
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	* Baeza-Yates, Ribeiro-Neto, Modern Information Retrieval, 2011, Addison-Wesley		* Baeza-Yates, Ribeiro-Neto, Modern Information Retrieval, 2011, Addison-Wesley
	* Buttcher, Clarke, Cormack, Information Retrieval: Implementing and Evaluating Search Engines, 2010, MIT Press		* Buttcher, Clarke, Cormack, Information Retrieval: Implementing and Evaluating Search Engines, 2010, MIT Press
−	* [~~Course repository in github](~~https://github.com/IUCVLab/information-retrieval).	+	* [https://github.com/IUCVLab/information-retrieval Course repository in github].

	=== Closed access resources ===		=== Closed access resources ===

S.protasov: Syllabus content is aligned with current state of the course.

2022-12-19T14:31:25Z

Syllabus content is aligned with current state of the course.

← Older revision		Revision as of 14:31, 19 December 2022
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	* '''Course name''': Information Retrieval		* '''Course name''': Information Retrieval
	* '''Code discipline''': CSE306		* '''Code discipline''': CSE306
−	* '''Subject area''': Data Science; Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web	+	* '''Subject area''': Data Science; Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web; Recommender systems

	== Short Description ==		== Short Description ==
		+	The course gives an introduction to practical and theoretical aspects of information search and recommender systems.
−	This course covers the following concepts: Indexing; Relevance; Ranking; Information retrieval; Query.	+	This course covers the following concepts: Indexing; Search quality assessment; Relevance; Ranking; Information retrieval; Query; Recommendations; Multimedia retrieval.

	== Prerequisites ==		== Prerequisites ==
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	# Content-based image retrieval		# Content-based image retrieval
	# Audio retrieval		# Audio retrieval
−	# Hum to search
	# Relevance feedback		# Relevance feedback
	\|}		\|}
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	! Activity Type !! Percentage of the overall course grade		! Activity Type !! Percentage of the overall course grade
	\|-		\|-
−	\| ~~Labs/seminar classes~~ \|\| 35	+	\| Assignments \|\| 60
	\|-		\|-
		+	\| Quizzes \|\| 40
−	\| Interim performance assessment \|\| 70
	\|-		\|-
	\| Exams \|\| 0		\| Exams \|\| 0
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	=== Recommendations for students on how to succeed in the course ===		=== Recommendations for students on how to succeed in the course ===

		+	The simples way to succeed is to participate in labs and pass coding assignments in timely manner. This guarantees up to 60% of the grade. Participation in lecture quizzes allow to differentiate the grade.

	== Resources, literature and reference materials ==		== Resources, literature and reference materials ==
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	* Baeza-Yates, Ribeiro-Neto, Modern Information Retrieval, 2011, Addison-Wesley		* Baeza-Yates, Ribeiro-Neto, Modern Information Retrieval, 2011, Addison-Wesley
	* Buttcher, Clarke, Cormack, Information Retrieval: Implementing and Evaluating Search Engines, 2010, MIT Press		* Buttcher, Clarke, Cormack, Information Retrieval: Implementing and Evaluating Search Engines, 2010, MIT Press
−	* Course repository in github.	+	* [Course repository in github](https://github.com/IUCVLab/information-retrieval).

	=== Closed access resources ===		=== Closed access resources ===
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	=== The retake exam ===		=== The retake exam ===
	'''Section 1'''		'''Section 1'''
		+	# Solve a complex coding problem similar to one of the homework or lab.
−
	'''Section 2'''		'''Section 2'''
		+	# Solve a complex coding problem similar to one of the homework or lab.
−
	'''Section 3'''		'''Section 3'''
		+	# Solve a complex coding problem similar to one of the homework or lab.
−
	'''Section 4'''		'''Section 4'''
		+	# Solve a complex coding problem similar to one of the homework or lab.

S.protasov: /* Information Retrieval */

2022-12-15T14:05:10Z

Information Retrieval

← Older revision		Revision as of 14:05, 15 December 2022
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	= Information Retrieval =		= Information Retrieval =
	* '''Course name''': Information Retrieval		* '''Course name''': Information Retrieval
−	* '''Code discipline''': ~~XYZ~~	+	* '''Code discipline''': CSE306
	* '''Subject area''': Data Science; Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web		* '''Subject area''': Data Science; Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web

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	=== Software and tools used within the course ===		=== Software and tools used within the course ===
		+
−
	= Teaching Methodology: Methods, techniques, & activities =		= Teaching Methodology: Methods, techniques, & activities =

R.sirgalina at 09:19, 12 July 2022

2022-07-12T09:19:50Z

Show changes

M.petrishchev at 13:00, 26 April 2022

2022-04-26T13:00:41Z

← Older revision		Revision as of 13:00, 26 April 2022
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	== Course Objectives Based on Bloom’s Taxonomy ==		== Course Objectives Based on Bloom’s Taxonomy ==

−	=== - What should a student remember at the end of the course? ===	+	=== What should a student remember at the end of the course? ===

	* Terms and definitions used in area of information retrieval,		* Terms and definitions used in area of information retrieval,
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	* Indexing strategies.		* Indexing strategies.

−	=== - What should a student be able to understand at the end of the course? ===	+	=== What should a student be able to understand at the end of the course? ===

	* Understand background theories behind information retrieval systems,		* Understand background theories behind information retrieval systems,
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	* How to identify and fix information retrieval system problems.		* How to identify and fix information retrieval system problems.

−	=== - What should a student be able to apply at the end of the course? ===	+	=== What should a student be able to apply at the end of the course? ===

	* Build a recommender service from scratch,		* Build a recommender service from scratch,

M.petrishchev at 13:00, 26 April 2022

2022-04-26T13:00:00Z

← Older revision		Revision as of 13:00, 26 April 2022
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−	=== Course Objectives Based on Bloom’s Taxonomy ===	+	== Course Objectives Based on Bloom’s Taxonomy ==

	=== - What should a student remember at the end of the course? ===		=== - What should a student remember at the end of the course? ===

M.petrishchev: M.petrishchev moved page BSc:InformationRetrieval to BSc: Information Retrieval

2022-04-18T08:52:59Z

M.petrishchev moved page BSc:InformationRetrieval to BSc: Information Retrieval

← Older revision	Revision as of 08:52, 18 April 2022
(No difference)

M.petrishchev: /* Prerequisites */

2022-04-05T08:00:02Z

Prerequisites

← Older revision		Revision as of 08:00, 5 April 2022
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	== Prerequisites ==		== Prerequisites ==

		+	Analytic Geometry And Linear Algebra I
−	* Linear? Algebra: matrix multiplication, matrix decomposition (SVD, ALS) and approximation (matrix norm), sparse matrix, stability of solution (decomposition), vector spaces, metric spaces, manifold, eigenvector and eigenvalue.	+	* [https://eduwiki.innopolis.university/index.php/BSc:AnalyticGeometryAndLinearAlgebraII CSE204 — Analytic Geometry And Linear Algebra II]: matrix multiplication, matrix decomposition (SVD, ALS) and approximation (matrix norm), sparse matrix, stability of solution (decomposition), vector spaces, metric spaces, manifold, eigenvector and eigenvalue.

−	* Discrete ~~math~~: graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path.	+	* [https://eduwiki.innopolis.university/index.php/BSc:Logic_and_Discrete_Mathematics CSE113 — Philosophy I - (Discrete Math and Logic)]: graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path.

−	* ~~ProbStat~~: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties. Analysis: DFT, [discrete] gradient.	+	* [https://eduwiki.innopolis.university/index.php/BSc:ProbabilityAndStatistics CSE206 — Probability And Statistics]: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties. Analysis: DFT, [discrete] gradient.

M.petrishchev at 07:25, 4 April 2022

2022-04-04T07:25:19Z

← Older revision		Revision as of 07:25, 4 April 2022
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	The course is designed to prepare students to understand background theories of information retrieval systems and introduce different information retrieval systems. The course will focus on the evaluation and analysis of such systems as well as how they are implemented. Throughout the course, students will be involved in discussions, readings, and assignments to experience real world systems. The technologies and algorithms covered in this class include machine learning, data mining, natural language processing, data indexing, and so on.		The course is designed to prepare students to understand background theories of information retrieval systems and introduce different information retrieval systems. The course will focus on the evaluation and analysis of such systems as well as how they are implemented. Throughout the course, students will be involved in discussions, readings, and assignments to experience real world systems. The technologies and algorithms covered in this class include machine learning, data mining, natural language processing, data indexing, and so on.
		+
		+	== Prerequisites ==
		+
		+	* Linear? Algebra: matrix multiplication, matrix decomposition (SVD, ALS) and approximation (matrix norm), sparse matrix, stability of solution (decomposition), vector spaces, metric spaces, manifold, eigenvector and eigenvalue.
		+
		+	* Discrete math: graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path.
		+
		+	* ProbStat: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties. Analysis: DFT, [discrete] gradient.
		+

	=== Course Objectives Based on Bloom’s Taxonomy ===		=== Course Objectives Based on Bloom’s Taxonomy ===