{"id":4271,"date":"2026-01-16T11:21:47","date_gmt":"2026-01-16T11:21:47","guid":{"rendered":"https:\/\/devserver.admin.uoc.gr\/damsl\/?page_id=4271"},"modified":"2026-01-16T11:23:25","modified_gmt":"2026-01-16T11:23:25","slug":"damsl-287-neural-networks-and-learning-of-hierarchical-representations","status":"publish","type":"page","link":"https:\/\/mscs.uoc.gr\/damsl\/damsl-287-neural-networks-and-learning-of-hierarchical-representations\/","title":{"rendered":"DAMSL-287 Neural Networks and Learning of Hierarchical Representations"},"content":{"rendered":"\t\t
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Type<\/strong><\/p>

Elective<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Course Code<\/strong><\/p>

DAMSL-287<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Teaching Semester<\/strong><\/p>

B semester<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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ECTS Credits<\/strong><\/p>

10<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Student Performance Evaluation<\/h6>

Homework and\/or Lab Assignments, Final Exam and\/or Project<\/p><\/div><\/div><\/div>

Prerequisite Courses<\/h6>

Linear Algebra, Probabilities<\/p><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Syllabus<\/span><\/h6>

The scientific activity of the last decade revealed many new directions and highly successful extensions of Neural Networks towards learning data representations for various perception systems. Representations of this kind are composed of many layers of nonlinear calculations (multilayer architectures) and are based on classic artificial neural networks. In recent years it has become evident that learning such multilayered representations can contribute to a significant improvement in perception systems performance. The purpose of this course is to present an introduction to artificial neural networks and in learning hierarchical representations based on those network structures. The course will focus on architectures, methodologies and algorithms, and will also include laboratory exercises.<\/p><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Learning Outcomes<\/span><\/h6>
  • Having attended and succeeded in the course, the student is able to describe the probabilistic foundations of deep generative models and gain knowledge about various model architectures, training algorithms and their underlying principles.\u00a0<\/b><\/li>
  • Having attended and succeeded in the course, the student is able to comprehend the application areas of deep generative models in fields like computer vision, language and speech processing.<\/li>
  • Having attended and succeeded in the course, the student is capable of applying learned concepts to implement and train deep generative models and utilize these models for tasks such as synthetic tabular data generation, time-series synthesis and generative image processing.<\/li>
  • Having attended and succeeded in the course, the student is able to analyze and compare different deep generative models, understanding their strengths and limitations and critically assess the performance of these models in various scenarios.<\/li>
  • Having attended and succeeded in the course, the student is able to develop new approaches in deep generative modeling and distill information from research papers and practical demonstrations to create innovative solutions.<\/li>
  • Having attended and succeeded in the course, the student is able to critically evaluate the effectiveness of deep generative models in real-world applications and assess the impact of these models in advancing the field of generative AI.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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    <\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

    Type Elective Course Code DAMSL-287 Teaching Semester B semester ECTS Credits 10 Student Performance Evaluation Homework and\/or Lab Assignments, Final Exam and\/or Project Prerequisite Courses Linear Algebra, Probabilities Syllabus The scientific activity of the last decade revealed many new directions and highly successful extensions of Neural Networks towards learning data representations for various perception systems. Representations of this kind are composed of many layers of nonlinear calculations (multilayer architectures) and are based on classic artificial neural networks. In recent years it has become evident that learning such multilayered representations can contribute to a significant improvement in perception systems performance. The purpose of this course is to present an introduction to artificial neural networks and in learning hierarchical representations based on those network structures. The course will focus on architectures, methodologies and algorithms, and will also include laboratory exercises. Learning Outcomes Having attended and succeeded in the course, the student is able to describe the probabilistic foundations of deep generative models and gain knowledge about various model architectures, training algorithms and their underlying principles.\u00a0 Having attended and succeeded in the course, the student is able to comprehend the application areas of deep generative models in fields like computer vision, language and speech processing. Having attended and succeeded in the course, the student is capable of applying learned concepts to implement and train deep generative models and utilize these models for tasks such as synthetic tabular data generation, time-series synthesis and generative image processing. Having attended and succeeded in the course, the student is able to analyze and compare different deep generative models, understanding their strengths and limitations and critically assess the performance of these models in various scenarios. Having attended and succeeded in the course, the student is able to develop new approaches in deep generative modeling and distill information from research papers and practical demonstrations to create innovative solutions. Having attended and succeeded in the course, the student is able to critically evaluate the effectiveness of deep generative models in real-world applications and assess the impact of these models in advancing the field of generative AI.<\/p>\n","protected":false},"author":194,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-4271","page","type-page","status-publish","hentry","post-no-thumbnail"],"acf":[],"_links":{"self":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4271","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/comments?post=4271"}],"version-history":[{"count":4,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4271\/revisions"}],"predecessor-version":[{"id":4302,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4271\/revisions\/4302"}],"wp:attachment":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/media?parent=4271"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}