MSc: Advanced Machine Learning - Revision history

R.sirgalina at 08:36, 29 August 2022

2022-08-29T08:36:00Z

R.sirgalina: /* What is the purpose of this course? */

2022-07-13T06:31:38Z

What is the purpose of this course?

← Older revision		Revision as of 06:31, 13 July 2022
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	This course is designed for graduate students to provide comprehensive and advance topics in machine learning. Student will learn to implement the machine learning models in Python programming environment from data science prospective. In this course, we will cover Neural Network Architectures (CNN, LSTM, Attention, RNN), Regularization, Genetic algorithm, Graphical Models (HMM), Generative models, Reinforcement learning, Collaborative filtering and recent trends in machine learning. The end of the day they will able to apply machine learning algorithms to solve real-world problems.		This course is designed for graduate students to provide comprehensive and advance topics in machine learning. Student will learn to implement the machine learning models in Python programming environment from data science prospective. In this course, we will cover Neural Network Architectures (CNN, LSTM, Attention, RNN), Regularization, Genetic algorithm, Graphical Models (HMM), Generative models, Reinforcement learning, Collaborative filtering and recent trends in machine learning. The end of the day they will able to apply machine learning algorithms to solve real-world problems.
−

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

M.petrishchev at 07:16, 25 April 2022

2022-04-25T07:16:23Z

← Older revision		Revision as of 07:16, 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? ===

	By the end of the course, the students should be able to apply machine learning models in real-world applications.		By the end of the course, the students should be able to apply machine learning models in real-world applications.
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	* Ability to analyze the models capacity and performances.		* Ability to analyze the models capacity and performances.

−	=== - 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 choose the correct machine learning model.		By the end of the course, the students should be able to choose the correct machine learning model.
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	* Ability to choose the right model for the given problem		* Ability to choose the right model for the given problem

−	=== - 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 deploy and developed models.		By the end of the course, the students should be able to deploy and developed models.

R.akhmetzyanov: /* Advanced Machine Learning */

2022-04-21T07:58:34Z

Advanced Machine Learning

← Older revision		Revision as of 07:58, 21 April 2022
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	* <span>'''Course name:'''</span> Advanced Machine Learning		* <span>'''Course name:'''</span> Advanced Machine Learning
	* <span>'''Course number:'''</span> DS-02		* <span>'''Course number:'''</span> DS-02
		⚫	== Prerequisites ==

	== Course Characteristics ==		== Course Characteristics ==
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	This course is designed for graduate students to provide comprehensive and advance topics in machine learning. Student will learn to implement the machine learning models in Python programming environment from data science prospective. In this course, we will cover Neural Network Architectures (CNN, LSTM, Attention, RNN), Regularization, Genetic algorithm, Graphical Models (HMM), Generative models, Reinforcement learning, Collaborative filtering and recent trends in machine learning. The end of the day they will able to apply machine learning algorithms to solve real-world problems.		This course is designed for graduate students to provide comprehensive and advance topics in machine learning. Student will learn to implement the machine learning models in Python programming environment from data science prospective. In this course, we will cover Neural Network Architectures (CNN, LSTM, Attention, RNN), Regularization, Genetic algorithm, Graphical Models (HMM), Generative models, Reinforcement learning, Collaborative filtering and recent trends in machine learning. The end of the day they will able to apply machine learning algorithms to solve real-world problems.

⚫	== Prerequisites ==

	=== Course objectives based on Bloom’s taxonomy ===		=== Course objectives based on Bloom’s taxonomy ===

R.akhmetzyanov: /* Prerequisites */

2022-04-21T07:23:41Z

Prerequisites

← Older revision		Revision as of 07:23, 21 April 2022
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	This course is designed for graduate students to provide comprehensive and advance topics in machine learning. Student will learn to implement the machine learning models in Python programming environment from data science prospective. In this course, we will cover Neural Network Architectures (CNN, LSTM, Attention, RNN), Regularization, Genetic algorithm, Graphical Models (HMM), Generative models, Reinforcement learning, Collaborative filtering and recent trends in machine learning. The end of the day they will able to apply machine learning algorithms to solve real-world problems.		This course is designed for graduate students to provide comprehensive and advance topics in machine learning. Student will learn to implement the machine learning models in Python programming environment from data science prospective. In this course, we will cover Neural Network Architectures (CNN, LSTM, Attention, RNN), Regularization, Genetic algorithm, Graphical Models (HMM), Generative models, Reinforcement learning, Collaborative filtering and recent trends in machine learning. The end of the day they will able to apply machine learning algorithms to solve real-world problems.

−	=== Prerequisites ===	+	== Prerequisites ==

	=== Course objectives based on Bloom’s taxonomy ===		=== Course objectives based on Bloom’s taxonomy ===

R.akhmetzyanov: /* What is the purpose of this course? */

2022-04-21T07:22:41Z

What is the purpose of this course?

← Older revision		Revision as of 07:22, 21 April 2022
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	This course is designed for graduate students to provide comprehensive and advance topics in machine learning. Student will learn to implement the machine learning models in Python programming environment from data science prospective. In this course, we will cover Neural Network Architectures (CNN, LSTM, Attention, RNN), Regularization, Genetic algorithm, Graphical Models (HMM), Generative models, Reinforcement learning, Collaborative filtering and recent trends in machine learning. The end of the day they will able to apply machine learning algorithms to solve real-world problems.		This course is designed for graduate students to provide comprehensive and advance topics in machine learning. Student will learn to implement the machine learning models in Python programming environment from data science prospective. In this course, we will cover Neural Network Architectures (CNN, LSTM, Attention, RNN), Regularization, Genetic algorithm, Graphical Models (HMM), Generative models, Reinforcement learning, Collaborative filtering and recent trends in machine learning. The end of the day they will able to apply machine learning algorithms to solve real-world problems.
		+
		+	=== Prerequisites ===

	=== Course objectives based on Bloom’s taxonomy ===		=== Course objectives based on Bloom’s taxonomy ===

M.petrishchev: M.petrishchev moved page MSc:Advanced Machine Learning to MSc: Advanced Machine Learning over redirect

2022-04-15T08:02:01Z

M.petrishchev moved page MSc:Advanced Machine Learning to MSc: Advanced Machine Learning over redirect

← Older revision	Revision as of 08:02, 15 April 2022
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M.petrishchev: M.petrishchev moved page MSc: Advanced Machine Learning to MSc:Advanced Machine Learning over redirect

2022-04-15T07:25:04Z

M.petrishchev moved page MSc: Advanced Machine Learning to MSc:Advanced Machine Learning over redirect

← Older revision	Revision as of 07:25, 15 April 2022
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M.petrishchev: M.petrishchev moved page MSc:Advanced Machine Learning to MSc: Advanced Machine Learning

2022-04-15T07:24:30Z

M.petrishchev moved page MSc:Advanced Machine Learning to MSc: Advanced Machine Learning

← Older revision	Revision as of 07:24, 15 April 2022
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M.petrishchev: M.petrishchev moved page MSc:AdvancedMachineLearning.S22 to MSc:Advanced Machine Learning

2022-04-10T12:30:43Z

M.petrishchev moved page MSc:AdvancedMachineLearning.S22 to MSc:Advanced Machine Learning

← Older revision	Revision as of 12:30, 10 April 2022
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