MSc: Advanced Information Retrieval - Revision history

S.protasov: /* Prerequisite subjects */

2022-12-19T14:35:38Z

Prerequisite subjects

← Older revision		Revision as of 14:35, 19 December 2022
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	* CSE113 — Philosophy I - (Discrete Math and Logic): graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path.		* CSE113 — Philosophy I - (Discrete Math and Logic): graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path.
	* CSE206 — Probability And Statistics: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.		* CSE206 — Probability And Statistics: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.
−	* Analysis: DFT, [discrete] gradient.
−	* 3blue1brown playlist on Linear Algebra can help to overview selected topics.
−	* Actually, on their channel you can find almost any maths topic, e.g. Fourier Transform.
−	* Gilbert Strang is one of the best human teachers of Algebra, if you prefer classic lectures to fancy videos.
−	* This MIT course can help you with discrete objects.
−	* For Russian readers there is a maths book from the course author with labs.
−	* Also there is a very basic python-based course on maths with lots of relevant (and irrelevant) labs.
−	* To have a better feeling of networking, please refer to this video lecture.
−	* Kick start your numpy with the official quickstart guide.

	=== Prerequisite topics ===		=== Prerequisite topics ===

S.protasov: Updated a syllabus

2022-12-19T14:35:08Z

Updated a syllabus

Show changes

R.sirgalina at 08:42, 29 August 2022

2022-08-29T08:42:20Z

Show changes

M.petrishchev: /* Course Objectives Based on Bloom’s Taxonomy */

2022-04-26T07:55:04Z

Course Objectives Based on Bloom’s Taxonomy

← Older revision		Revision as of 07:55, 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? ===

	By the end of the course, the students should be able to remember and recognize		By the end of the course, the students should be able to remember and recognize
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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? ===

	By the end of the course, the students should be able to describe and explain		By the end of the course, the students should be able to describe and explain
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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? ===

	By the end of the course, the students should be able to		By the end of the course, the students should be able to

M.petrishchev at 07:17, 25 April 2022

2022-04-25T07:17:26Z

← Older revision		Revision as of 07:17, 25 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? ===

R.akhmetzyanov: /* Prerequisites */

2022-04-22T13:21:22Z

Prerequisites

← Older revision		Revision as of 13:21, 22 April 2022
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	== Prerequisites ==		== Prerequisites ==

−	* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101] — Introduction to Programming I ~~and [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102] — Introduction to Programming II~~	+	* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101] — Introduction to Programming I
		+	* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102] — Introduction to Programming II
⚫	* ~~[https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I and~~ [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II 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.
		+	* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I
		⚫	* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II 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.
	* [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.		* [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.
	* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics CSE206 — Probability And Statistics]: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.		* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics CSE206 — Probability And Statistics]: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.

M.petrishchev: /* Prerequisites */

2022-04-21T07:41:59Z

Prerequisites

← Older revision		Revision as of 07:41, 21 April 2022
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	* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I and [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II 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.		* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I and [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II 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.
	* [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.		* [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.
−	* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics CSE206] — Probability And Statistics: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.	+	* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics CSE206 — Probability And Statistics]: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.
	* Analysis: DFT, [discrete] gradient.		* Analysis: DFT, [discrete] gradient.

M.petrishchev: /* Prerequisites */

2022-04-21T06:33:32Z

Prerequisites

← Older revision		Revision as of 06:33, 21 April 2022
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	== Prerequisites ==		== Prerequisites ==

−	* CSE101 — Introduction to Programming I and CSE102 — Introduction to Programming II	+	* [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming CSE101] — Introduction to Programming I and [https://eduwiki.innopolis.university/index.php/BSc:_Introduction_To_Programming_II CSE102] — Introduction to Programming II
−	* CSE202 — Analytical Geometry and Linear Algebra I and 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.	+	* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I and [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II 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.
−	* CSE113 — Philosophy I - (Discrete Math and Logic): 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.
−	* CSE206 — Probability And Statistics: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.	+	* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics CSE206] — Probability And Statistics: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.
	* Analysis: DFT, [discrete] gradient.		* Analysis: DFT, [discrete] gradient.

R.akhmetzyanov: /* Prerequisites */

2022-04-18T07:03:36Z

Prerequisites

← Older revision		Revision as of 07:03, 18 April 2022
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	== Prerequisites ==		== Prerequisites ==

		+	* CSE101 — Introduction to Programming I and CSE102 — Introduction to Programming II
−	The course will benefit if students already know some topics of mathematics and programming.
		⚫	* CSE202 — Analytical Geometry and Linear Algebra I and 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.
−
		⚫	* CSE113 — Philosophy I - (Discrete Math and Logic): graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path.
−	* Programming: python, numpy library and basic programming for internet skills (HTTP requests, networking).
		⚫	* CSE206 — Probability And Statistics: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.
−
−	Mathematics (Algebra):
−
⚫	* ~~[https://eduwiki.innopolis.university/index.php/BSc:AnalyticGeometryAndLinearAlgebraI~~ CSE202 — Analytical Geometry and Linear Algebra I] ~~& [https://eduwiki.innopolis.university/index.php/BSc:AnalyticGeometryAndLinearAlgebraII~~ CSE204 — ~~Analytical~~ 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.
−
⚫	* ~~[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.
−
⚫	* ~~[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.		* Analysis: DFT, [discrete] gradient.

M.petrishchev: M.petrishchev moved page MSc:Advanced Information Retrieval to MSc: Advanced Information Retrieval

2022-04-15T08:33:30Z

M.petrishchev moved page MSc:Advanced Information Retrieval to MSc: Advanced Information Retrieval

← Older revision	Revision as of 08:33, 15 April 2022
(No difference)