BSc: Introduction To Machine Learning - Revision history

R.sirgalina at 09:57, 12 July 2022

2022-07-12T09:57:44Z

M.petrishchev: /* Prerequisites */

2022-04-21T07:40:59Z

Prerequisites

← Older revision		Revision as of 07:40, 21 April 2022
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	Programming:		Programming:
−	* CSE117 — Data Structures and Algorithms: python, numpy, basic object-oriented concepts, memory management.	+	* [https://eduwiki.innopolis.university/index.php/BSc:_Data_Structures_Algorithms CSE117] — Data Structures and Algorithms: python, numpy, basic object-oriented concepts, memory management.

	For a more concrete identification of subtopics, please see chapters 2, 3 and 4 of (1), which has done an excellent job in listing and describing all important maths subtopics essential for machine learning students. In addition to that, students are strongly advised to gain a basic understanding of descriptive statistics and data distributions, statistical hypothesis testing, and data sampling, resampling, and experimental design techniques.		For a more concrete identification of subtopics, please see chapters 2, 3 and 4 of (1), which has done an excellent job in listing and describing all important maths subtopics essential for machine learning students. In addition to that, students are strongly advised to gain a basic understanding of descriptive statistics and data distributions, statistical hypothesis testing, and data sampling, resampling, and experimental design techniques.

M.petrishchev: /* Prerequisites */

2022-04-21T06:30:26Z

Prerequisites

← Older revision		Revision as of 06:30, 21 April 2022
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	Maths:		Maths:
−	* CSE202 — Analytical Geometry and Linear Algebra I	+	* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_I1 CSE202] — Analytical Geometry and Linear Algebra I
−	* CSE204 — Analytical Geometry and Linear Algebra II	+	* [https://eduwiki.innopolis.university/index.php/BSc:_Analytic_Geometry_And_Linear_Algebra_II CSE204] — Analytical Geometry and Linear Algebra II
−	* CSE201 — Mathematical Analysis I	+	* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_I CSE201] — Mathematical Analysis I
−	* CSE203 — Mathematical Analysis II	+	* [https://eduwiki.innopolis.university/index.php/BSc:_Mathematical_Analysis_II CSE203] — Mathematical Analysis II
−	* CSE206 — Probability And Statistics	+	* [https://eduwiki.innopolis.university/index.php/BSc:_Probability_And_Statistics CSE206] — Probability And Statistics

	Programming:		Programming:

R.akhmetzyanov: /* Prerequisites */

2022-04-18T06:57:12Z

Prerequisites

← Older revision		Revision as of 06:57, 18 April 2022
Line 19:		Line 19:

	Maths:		Maths:
−	* ~~[https://eduwiki.innopolis.university/index.php/BSc:AnalyticGeometryAndLinearAlgebraI~~ CSE202 — Analytical Geometry and Linear Algebra I]	+	* CSE202 — Analytical Geometry and Linear Algebra I
−	* ~~[https://eduwiki.innopolis.university/index.php/BSc:AnalyticGeometryAndLinearAlgebraII~~ CSE204 — Analytical Geometry and Linear Algebra II]	+	* CSE204 — Analytical Geometry and Linear Algebra II
−	* ~~[https://eduwiki.innopolis.university/index.php/BSc:MathematicalAnalysisI~~ CSE201 — Mathematical Analysis I]	+	* CSE201 — Mathematical Analysis I
−	* ~~[https://eduwiki.innopolis.university/index.php/BSc:MathematicalAnalysisII~~ CSE203 — Mathematical Analysis II]	+	* CSE203 — Mathematical Analysis II
−	* ~~[https://eduwiki.innopolis.university/index.php/BSc:ProbabilityAndStatistics~~ CSE206 — Probability And Statistics]	+	* CSE206 — Probability And Statistics

	Programming:		Programming:
−	* ~~[https://eduwiki.innopolis.university/index.php/BSc:DataStructuresAlgorithms~~ CSE117 — Data Structures and Algorithms], python, numpy, basic object-oriented concepts, memory management.	+	* CSE117 — Data Structures and Algorithms: python, numpy, basic object-oriented concepts, memory management.

	For a more concrete identification of subtopics, please see chapters 2, 3 and 4 of (1), which has done an excellent job in listing and describing all important maths subtopics essential for machine learning students. In addition to that, students are strongly advised to gain a basic understanding of descriptive statistics and data distributions, statistical hypothesis testing, and data sampling, resampling, and experimental design techniques.		For a more concrete identification of subtopics, please see chapters 2, 3 and 4 of (1), which has done an excellent job in listing and describing all important maths subtopics essential for machine learning students. In addition to that, students are strongly advised to gain a basic understanding of descriptive statistics and data distributions, statistical hypothesis testing, and data sampling, resampling, and experimental design techniques.

M.petrishchev: M.petrishchev moved page BSc:IntroductionToMachineLearning to BSc: Introduction To Machine Learning

2022-04-14T13:58:30Z

M.petrishchev moved page BSc:IntroductionToMachineLearning to BSc: Introduction To Machine Learning

← Older revision	Revision as of 13:58, 14 April 2022
(No difference)

M.petrishchev: /* Prerequisites */

2022-04-14T13:56:17Z

Prerequisites

← Older revision		Revision as of 13:56, 14 April 2022
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	== Prerequisites ==		== Prerequisites ==
	The course will benefit if students already know some topics of mathematics and programming.		The course will benefit if students already know some topics of mathematics and programming.
−
⚫	Programming:
⚫	* [https://eduwiki.innopolis.university/index.php/BSc:DataStructuresAlgorithms CSE117 — Data Structures and Algorithms], python, numpy, basic object-oriented concepts, memory management,

	Maths:		Maths:
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	* [https://eduwiki.innopolis.university/index.php/BSc:ProbabilityAndStatistics CSE206 — Probability And Statistics]		* [https://eduwiki.innopolis.university/index.php/BSc:ProbabilityAndStatistics CSE206 — Probability And Statistics]

		⚫	Programming:
		⚫	* [https://eduwiki.innopolis.university/index.php/BSc:DataStructuresAlgorithms CSE117 — Data Structures and Algorithms], python, numpy, basic object-oriented concepts, memory management.

	For a more concrete identification of subtopics, please see chapters 2, 3 and 4 of (1), which has done an excellent job in listing and describing all important maths subtopics essential for machine learning students. In addition to that, students are strongly advised to gain a basic understanding of descriptive statistics and data distributions, statistical hypothesis testing, and data sampling, resampling, and experimental design techniques.		For a more concrete identification of subtopics, please see chapters 2, 3 and 4 of (1), which has done an excellent job in listing and describing all important maths subtopics essential for machine learning students. In addition to that, students are strongly advised to gain a basic understanding of descriptive statistics and data distributions, statistical hypothesis testing, and data sampling, resampling, and experimental design techniques.

M.petrishchev: /* Prerequisites */

2022-04-14T13:49:16Z

Prerequisites

@@ Line 17: / Line 17: @@
 == Prerequisites ==
 The course will benefit if students already know some topics of mathematics and programming.
 == Course Objectives Based on Bloom’s Taxonomy ==

N.askarbekuly at 07:59, 14 April 2022

2022-04-14T07:59:32Z

← Older revision		Revision as of 07:59, 14 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 recognize and define		By the end of the course, the students should be able to recognize and define
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	* Popular machine learning software tools		* Popular machine learning software tools

−	=== - 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 (with examples)		By the end of the course, the students should be able to describe and explain (with examples)
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	* Neural or Deep Learning		* Neural or Deep Learning

−	=== - 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 apply		By the end of the course, the students should be able to apply

N.askarbekuly at 07:59, 14 April 2022

2022-04-14T07:59:06Z

← Older revision		Revision as of 07:59, 14 April 2022
Line 23:		Line 23:


−	=== 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: /* Prerequisites */

2022-04-08T13:55:22Z

Prerequisites

← Older revision		Revision as of 13:55, 8 April 2022
Line 18:		Line 18:
	The course will benefit if students already know some topics of mathematics and programming.		The course will benefit if students already know some topics of mathematics and programming.
	* Programming: Python, numpy, basic object-oriented concepts, memory management, data structures, and algorithms.		* Programming: Python, numpy, basic object-oriented concepts, memory management, data structures, and algorithms.
−	* Maths: A thorough understanding of mathematics (linear algebra, calculus, probability theory and statistics) is necessary to gain a solid understanding of the internal working of machine learning algorithms. For a more concrete identification of subtopics, please see chapters 2, 3 and 4 of [1], which has done an excellent job in listing and describing all important maths subtopics essential for machine learning students. In addition to that, students are strongly advised to gain a basic understanding of descriptive statistics and data distributions, statistical hypothesis testing, and data sampling, resampling, and experimental design techniques	+	* Maths: A thorough understanding of mathematics (linear algebra, calculus, probability theory and statistics) is necessary to gain a solid understanding of the internal working of machine learning algorithms. For a more concrete identification of subtopics, please see chapters 2, 3 and 4 of (1), which has done an excellent job in listing and describing all important maths subtopics essential for machine learning students. In addition to that, students are strongly advised to gain a basic understanding of descriptive statistics and data distributions, statistical hypothesis testing, and data sampling, resampling, and experimental design techniques

	[https://www.deeplearningbook.org/ (1) Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning. MIT Press].		[https://www.deeplearningbook.org/ (1) Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning. MIT Press].