{"id":4205,"date":"2026-01-16T10:17:00","date_gmt":"2026-01-16T10:17:00","guid":{"rendered":"https:\/\/devserver.admin.uoc.gr\/damsl\/?page_id=4205"},"modified":"2026-01-16T10:19:20","modified_gmt":"2026-01-16T10:19:20","slug":"damsl-101-introduction-to-machine-learning","status":"publish","type":"page","link":"https:\/\/mscs.uoc.gr\/damsl\/damsl-101-introduction-to-machine-learning\/","title":{"rendered":"DAMSL-101 Introduction to Machine Learning"},"content":{"rendered":"\t\t
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Type<\/strong><\/p>

Core<\/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-101<\/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>

A 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>

Probabilities, Data Structures<\/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>
  • Introduction to ML, supervised, unsupervised, reinforcement learning, hypothesis (models) spaces, examples of ML applications<\/li>
  • Probability theory and concepts for ML, axioms of probability, conditional probability, Bayes theorem, maximum likelihood estimation, maximum a posteriori estimation<\/li>
  • Logistic Regression and fitting with gradient descent<\/li>
  • Hypothesis testing, and permutation-based hypothesis testing<\/li>
  • Na\u00efve Bayes<\/li>
  • Decision Trees and Random Forests<\/li>
  • Metrics of performance, Receiver Operating Characteristic Curves (ROC), and Area Under the ROC curve<\/li>
  • Estimation of performance, hyper-parameter tuning, and introduction to Automated Machine Learning (AutoML)<\/li>
  • Basics of optimization and constrained optimization and Support Vector Machine classification<\/li>
  • Introduction to causal discovery and causal-based feature Selection<\/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
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    Learning Outcomes<\/span><\/h6>

    The purpose of the course is to provide a broad introduction to the field of Machine Learning including the basic theory, principles, and algorithms, as well as practical applications on real problems. Upon successful completion of this course students should be able to:<\/p>

    • Learn basic ML tasks and types of analysis, such as supervised learning, unsupervised learning, reinforcement learning, classification and regression, and feature selection.<\/li>
    • Understand the inner workings of standard ML classification and feature selection algorithms<\/li>
    • Learn how to solve the problem of selecting algorithms, tuning their hyper-parameters, and estimating the performance of the final predictive model.<\/li>
    • Be able to perform and apply ML pipelines to real-world problems, dealing with problems such as representing the problems as an ML task, representing appropriately the data, applying and tuning an ML pipeline, and interpreting results.<\/li>
    • Know key statistical estimation and hypothesis testing concepts, with a focus on the ones that are routinely employed within ML algorithms<\/li>
    • Have a solid, foundational basis to perform ML research and proceed with other courses that employ ML algorithms and concepts<\/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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      Type Core Course Code DAMSL-101 Teaching Semester A semester ECTS Credits 10 Student Performance Evaluation Homework and\/or Lab assignments, Final exam and\/or project Prerequisite Courses Probabilities, Data Structures Syllabus Introduction to ML, supervised, unsupervised, reinforcement learning, hypothesis (models) spaces, examples of ML applications Probability theory and concepts for ML, axioms of probability, conditional probability, Bayes theorem, maximum likelihood estimation, maximum a posteriori estimation Logistic Regression and fitting with gradient descent Hypothesis testing, and permutation-based hypothesis testing Na\u00efve Bayes Decision Trees and Random Forests Metrics of performance, Receiver Operating Characteristic Curves (ROC), and Area Under the ROC curve Estimation of performance, hyper-parameter tuning, and introduction to Automated Machine Learning (AutoML) Basics of optimization and constrained optimization and Support Vector Machine classification Introduction to causal discovery and causal-based feature Selection Learning Outcomes The purpose of the course is to provide a broad introduction to the field of Machine Learning including the basic theory, principles, and algorithms, as well as practical applications on real problems. Upon successful completion of this course students should be able to: Learn basic ML tasks and types of analysis, such as supervised learning, unsupervised learning, reinforcement learning, classification and regression, and feature selection. Understand the inner workings of standard ML classification and feature selection algorithms Learn how to solve the problem of selecting algorithms, tuning their hyper-parameters, and estimating the performance of the final predictive model. Be able to perform and apply ML pipelines to real-world problems, dealing with problems such as representing the problems as an ML task, representing appropriately the data, applying and tuning an ML pipeline, and interpreting results. Know key statistical estimation and hypothesis testing concepts, with a focus on the ones that are routinely employed within ML algorithms Have a solid, foundational basis to perform ML research and proceed with other courses that employ ML algorithms and concepts<\/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-4205","page","type-page","status-publish","hentry","post-no-thumbnail"],"acf":[],"_links":{"self":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4205","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=4205"}],"version-history":[{"count":4,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4205\/revisions"}],"predecessor-version":[{"id":4221,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4205\/revisions\/4221"}],"wp:attachment":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/media?parent=4205"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}