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@@ -22,6 +22,19 @@ o On Thursdays we have a regular lecture. These lectures start at 1215pm and end
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The first week we start with simple linear regression, a repetition of
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linear algebra and elements of statistics needed for the course.
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!split
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===== AI/ML and some statements you may have heard (and what do they mean?) =====
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o Fei-Fei Li on ImageNet: _map out the entire world of objects_ ("The data that transformed AI research":"https://cacm.acm.org/news/219702-the-data-that-transformed-ai-research-and-possibly-the-world/fulltext")
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o Russell and Norvig in their popular textbook: _relevant to any intellectual task; it is truly a universal field_ ("Artificial Intelligence, A modern approach":"http://aima.cs.berkeley.edu/")
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o Woody Bledsoe puts it more bluntly: _in the long run, AI is the only science_ (quoted in Pamilla McCorduck, "Machines who think":"https://www.pamelamccorduck.com/machines-who-think")
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If you wish to have a critical read on AI/ML from a societal point of view, see "Kate Crawford's recent text Atlas of AI":"https://www.katecrawford.net/"
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_Here: with AI/ML we intend a collection of machine learning methods with an emphasis on statistical learning and data analysis_
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!split
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===== Schedule first week =====
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@@ -118,6 +131,19 @@ Projects are handed in using _Canvas_. We use Github as repository for codes, be
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!eblock
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!split
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===== Grading and deadlines =====
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!bblock Activity and Percentage of total score
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o First project, _due October 9_, 1/3%
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o Second project, _due November 6_, 1/3%
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o Third project, _due December 11_, 1/3%
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o Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due Friday same week), 20% additional score
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!eblock
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The extra credit assignments are due each Friday and can be uploaed to _canvas_ in your preferred format (although we prefer jupyter-notebooks). First assignment is for week 35.
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!split
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===== Recommended textbooks =====
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@@ -2606,3 +2632,6 @@ Add now a model which allows you to make polynomials up to degree $15$. Perform
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