Course in Bachelor’s program (3rd year, series E)<\/p>\n
Lect. \u0218erban Mihalache<\/strong><\/a> This course provides a fundamental perspective on the field of machine learning: the K<\/em>-means Model (KMM), the K<\/em>-nearest Neighbors algorithm (KNN), Gaussian Mixture Models (GMM), Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and fundamental artificial neural networks, particularly Fully-connected Neural Networks (FCNN). Additionally, an introduction to the field of deep learning is offered: Convolutional Neural Networks (CNN), Residual Neural Networks (ResNet), recurrent neural networks, LSTM cells, and advanced training techniques \u2013 regularization, dropout, batch normalization, etc. Course<\/strong><\/p>\n Laboratory<\/strong><\/p>\n Laboratory evaluation (continuous): 20% Course in Bachelor’s program (3rd year, series E) Teachers Lect. \u0218erban Mihalache Cristian-Teodor Neam\u021bu Course Description This course provides a fundamental perspective on the field of machine learning: the K-means Model (KMM), the K-nearest Neighbors algorithm (KNN), Gaussian Mixture Models (GMM), Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and fundamental artificial neural<\/p>\n","protected":false},"author":37,"featured_media":0,"parent":14,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-4696","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/staging.speed.pub.ro\/speed3\/wp-json\/wp\/v2\/pages\/4696","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/staging.speed.pub.ro\/speed3\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/staging.speed.pub.ro\/speed3\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/staging.speed.pub.ro\/speed3\/wp-json\/wp\/v2\/users\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/staging.speed.pub.ro\/speed3\/wp-json\/wp\/v2\/comments?post=4696"}],"version-history":[{"count":9,"href":"https:\/\/staging.speed.pub.ro\/speed3\/wp-json\/wp\/v2\/pages\/4696\/revisions"}],"predecessor-version":[{"id":4858,"href":"https:\/\/staging.speed.pub.ro\/speed3\/wp-json\/wp\/v2\/pages\/4696\/revisions\/4858"}],"up":[{"embeddable":true,"href":"https:\/\/staging.speed.pub.ro\/speed3\/wp-json\/wp\/v2\/pages\/14"}],"wp:attachment":[{"href":"https:\/\/staging.speed.pub.ro\/speed3\/wp-json\/wp\/v2\/media?parent=4696"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nCristian-Teodor Neam\u021bu<\/strong><\/a><\/p>\nCourse Description<\/h3>\n
\n\u2013 The lectures offer an introduction to the specific problems in the field and addresses the three fundamental paradigms (supervised learning, unsupervised learning, and reinforcement learning), providing a comparative analysis of these and the types of applications that are natively suited to each. The most important machine learning techniques and methods are presented, tailored for clustering, supervised classification, and regression problems (KMM, KNN, GMM, DT, RF, SVM, FCNN, CNN, and ResNet).
\n\u2013 The laboratory begins with a comprehensive introduction to using the Python programming language and the essential packages used in the field of machine learning (numpy<\/em>, scipy<\/em>, pandas<\/em>, matplotlib<\/em>, etc.). The remaining sessions cover the practical implementation of the main machine learning models studied in the lectures, using the scikit-learn<\/em> and Keras<\/em>\/TensorFlow<\/em> Python packages, and their use for various clustering, supervised classification, and regression applications.<\/p>\nContents<\/h3>\n
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Grading<\/h3>\n
\nLaboratory evaluation (colloquium): 20%
\nCourse midterm test (written): 10%
\nCourse final exam (written): 50%<\/p>\n","protected":false},"excerpt":{"rendered":"