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<title>Universitt Liechtenstein / C19 Advanced Machine Learning (WS 21/22)</title>
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                  <th width="20%" align="left" nowrap>Modulcode</th>
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                  <td valign="top"><h2 style="margin: 0px; padding: 0px;">C19 Advanced Machine Learning<!----></h2></td>
				  
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                  <td valign="top" nowrap>WS 21/22</td>
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Modul<!----></td>
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Wahl-/Pflichtmodul<!----></td>
                  <td valign="top" nowrap><!---->30.0 L<!----><!----> / 22.5 h<!----></td>
                  <td valign="top" nowrap><!---->67.5 h<!----></td>
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        <!----><h2>Lehrveranstaltungen</h2><!----><A HREF="../00022200/06303967.htm">Advanced Machine Learning 9690 1 WS 21/22, Vorlesung</A><br/><!---->

        <!----><h2>Modulleitung</h2><!----><!----><!---->Schneider, Johannes<br><!----><!----><!----><!----><!----><!----><!---->
       	<!----><h2>Studienplan</h2><!---->MSc WI 19<br><!---->
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        <!----><h2>Lehrinhalte</h2>
        <i>Advanced Machine Learning</i> covers several advanced topics in the field of machine learning and is concerned with requirements engineering in particular. Students learn to analyse certain types and large amounts of data. The course covers seven primary topics:<br/><br/><ul style='margin-top:0px; margin-bottom:0px'><li>Requirements engineering for machine learning and business intelligence projects</li><li>Frequent patterns and association rules</li><li>Explaining decisions of machine learning models</li><li>Time series analysis</li><li>Anomaly detection</li><li>Fundamentals of computational efficiency and distributed and parallel computing</li><li>Hadoop ecosystems, with a focus on Spark and MLlib</li></ul><p/><!---->
    
        <!----><h2>Lernergebnisse</h2>
        <ul style='margin-top:0px; margin-bottom:0px'><li>After successful completion of the course, students will:have deepened their understanding in the field of machine learning and acquired a larger set of machine-learning techniquesunderstand the challenges and solutions of processing large amounts of databe able to gather requirements for projects in the field of machine learning and business intelligence</li></ul><p/><!---->
     
        <!----><h2>Lehrmethode</h2>
        <ul style='margin-top:0px; margin-bottom:0px'><li>The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.</li></ul><p/><!---->
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        <!----><h2>Voraussetzungen</h2>
        <ul style='margin-top:0px; margin-bottom:0px'><li>Successful completion of the <i>Data Science</i> course or any other basic course on data science, data mining or machine learning. Exceptions are possible only after consultation with the lecturer and the study program management.</li></ul><p/><!---->
         <!----><h2>Lehrmittel</h2>
        <ul style='margin-top:0px; margin-bottom:0px'><li>Compulsory reading:Witten, H., Eibe, F., & Hall, M. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Amsterdam, The Netherlands: Elsevier.Aggarwal, C.C. (2015). Data Mining: The Textbook. Heidelberg, Germany: Springer.</li></ul><p/><!---->
    
        <!----><h2>Prfungsmethode</h2>
        Written exam (60min)<p/><!---->
    
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        <p><small>&nbsp;<br>&Auml;nderungen vorbehalten</small></p>
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