{"id":4206,"date":"2026-01-16T10:20:14","date_gmt":"2026-01-16T10:20:14","guid":{"rendered":"https:\/\/devserver.admin.uoc.gr\/damsl\/?page_id=4206"},"modified":"2026-01-16T10:22:05","modified_gmt":"2026-01-16T10:22:05","slug":"damsl-102-mathematical-and-computational-statistics","status":"publish","type":"page","link":"https:\/\/mscs.uoc.gr\/damsl\/damsl-102-mathematical-and-computational-statistics\/","title":{"rendered":"DAMSL-102 Mathematical and Computational Statistics"},"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-102<\/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\/Lab Assignments Final Exam<\/p><\/div><\/div><\/div>

Prerequisite Courses<\/h6>

Calculus I, Linear Algebra I, 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>
  • Introduction to R\u00a0<\/li>
  • Introduction to Resampling Methods: Cross-Validation<\/li>
  • Simulation of Random Variables Monte Carlo Experiments<\/li>
  • Bootstrapping Permutation Tests\u00a0<\/li>
  • Simulation of Random Variables<\/li>
  • Numerical Solution of Least Squares Problems Iterative Procedures for Model Building\u00a0<\/li>
  • Multimodel Inference\u00a0<\/li>
  • High-Dimensional Data; Regression in High Dimensions Lasso-Type Estimators<\/li>
  • Numerical Solution of Maximum Likelihood Equations\u00a0<\/li>
  • Newton-Raphson Algorithm\u00a0<\/li>
  • Generalized Linear Models; The Fisher Scoring Algorithm<\/li><\/ul><\/div><\/div><\/div>
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    Learning Outcomes<\/span><\/h6>
    • Upon successful completion of this course students will have a very good knowledge of R
      software.<\/li>
    • Furthermore, students will be able to: simulate random numbers from a wide variety of
      probability distributions; construct confidence intervals using bootstrap; perform hypothesis
      testing via permutations; conduct Monte Carlo experiments to evaluate alternative estimators;
      evaluate predictive models using cross-validation.<\/li>
    • Upon successful completion of this course students will have understood the algorithms for the
      numerical solution of least squares problems and will be able to create their own functions for
      stepwise model building.<\/li>
    • Upon successful completion of this course students will have learned algorithms for penalized
      estimation of least squares and least absolute deviations problems and will be able to evaluate
      lasso-type estimators via Monte Carlo experiments.<\/li>
    • Upon successful completion of this course students will be able to solve numerically maximum likelihood problems using the Newton-Raphson and Fisher-Scoring Algorithms.<\/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 Core Course Code DAMSL-102 Teaching Semester B semester ECTS Credits 10 Student Performance Evaluation Homework\/Lab Assignments Final Exam Prerequisite Courses Calculus I, Linear Algebra I, Probabilities Syllabus Introduction to R\u00a0 Introduction to Resampling Methods: Cross-Validation Simulation of Random Variables Monte Carlo Experiments Bootstrapping Permutation Tests\u00a0 Simulation of Random Variables Numerical Solution of Least Squares Problems Iterative Procedures for Model Building\u00a0 Multimodel Inference\u00a0 High-Dimensional Data; Regression in High Dimensions Lasso-Type Estimators Numerical Solution of Maximum Likelihood Equations\u00a0 Newton-Raphson Algorithm\u00a0 Generalized Linear Models; The Fisher Scoring Algorithm \u00a0 Learning Outcomes Upon successful completion of this course students will have a very good knowledge of Rsoftware. Furthermore, students will be able to: simulate random numbers from a wide variety ofprobability distributions; construct confidence intervals using bootstrap; perform hypothesistesting via permutations; conduct Monte Carlo experiments to evaluate alternative estimators;evaluate predictive models using cross-validation. Upon successful completion of this course students will have understood the algorithms for thenumerical solution of least squares problems and will be able to create their own functions forstepwise model building. Upon successful completion of this course students will have learned algorithms for penalizedestimation of least squares and least absolute deviations problems and will be able to evaluatelasso-type estimators via Monte Carlo experiments. Upon successful completion of this course students will be able to solve numerically maximum likelihood problems using the Newton-Raphson and Fisher-Scoring Algorithms.<\/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-4206","page","type-page","status-publish","hentry","post-no-thumbnail"],"acf":[],"_links":{"self":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4206","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=4206"}],"version-history":[{"count":4,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4206\/revisions"}],"predecessor-version":[{"id":4225,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4206\/revisions\/4225"}],"wp:attachment":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/media?parent=4206"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}