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Modern Data Analysis 2021 2022 - История изменений
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imported>Machine: /* Homeworks */
2021-11-01T11:34:54Z
<p><span class="autocomment">Homeworks</span></p>
<p><b>Новая страница</b></p><div>== Course: Modern Data Analysis (2021–2022) ==<br />
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'''Lecturer:''' Dmitry Ignatov<br />
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'''TA:''' TBA<br />
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All the material are available via our telegram channel. <br />
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'''Final mark formula''': FM = 0.8 Homeworks + 0.2 Exam (under voting)<br />
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=== Homeworks ===<br />
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* Homework 1: Classification<br />
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'''Announced''': 11.10.2021<br />
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'''Soft deadline''': changed to 08.11.2021 due to pandemic regulations (before it was 03.11.2021)<br />
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'''Hard deadline''': 10.11.2021<br />
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=== Lectures ===<br />
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==== Lecture 1====<br />
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Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch?<br />
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Practice: demonstration with Orange.<br />
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==== Lecture 2====<br />
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Classification. One-rule. Naïve Bayes. kNN. Logistic Regression. Train-test split and cross-validation. Quality Metrics (TP, FP, TN, FN, Precision, Recall, F-measure, Accuracy).<br />
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Practice: demonstration with Orange.<br />
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==== Lecture 3====<br />
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Classification (continued). Quality metrics. ROC curves. <br />
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Practice: demonstration with Orange.<br />
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==== Seminar 1====<br />
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Classification <br />
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Practice: scikit-learn.<br />
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==== Lecture 4====<br />
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Introduction to Clustering. Taxonomy of clustering methods. K-means. K-medoids. Fuzzy C-means. Types of distance metrics. Hierarchical clustering. DBScan<br />
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Practice: DBScan Demo.</div>
imported>Machine