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24cs500 [2025-02-24] – [Synopsis] Martin Ziegler24cs500 [2025-06-26] (current) – [Syllabus/Slides] Martin Ziegler
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 All Computer Science is based on the concept of an efficient //algorithm//: as opposed to a //program// or a //heuristic//. All Computer Science is based on the concept of an efficient //algorithm//: as opposed to a //program// or a //heuristic//.
-We thus call these 'virtues' and features distinguishing CS from EE (Electrical Engineering):+We thus call these 'virtues' and features distinguishing CS (=Science) from EE (=Engineering):
  
   *     unambiguous problem specification   *     unambiguous problem specification
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 ==== Syllabus/Slides ==== ==== Syllabus/Slides ====
 +
 +This course (pun) introduces "appetizer" chapters 
 +on some of the many topics in contemporary //Algorithm Design and Analysis//: \\
 +as an overview and to make you "hungry" for later perhaps pursuing a PhD in this field. \\
 +
 1.  Recap, Intro & Overview  ({{:cs500a.ppt|PPT}}, {{:cs500a.pdf|PDF}}) \\ 1.  Recap, Intro & Overview  ({{:cs500a.ppt|PPT}}, {{:cs500a.pdf|PDF}}) \\
-9.  Parallel Algorithms ({{:cs500i.ppt|PPT}}, {{:cs500i.pdf|PDF}}) \\ +9.  Parallel Algorithms ({{:cs500p.ppt|PPT}}, {{:cs500p.pdf|PDF}}) \\ 
-10.  Memory-Efficient Algorithms ({{:cs500k.ppt|PPT}}, {{:cs500k.pdf|PDF}}) \\+10.  Memory-Efficient Algorithms ({{:cs500m.ppt|PPT}}, {{:cs500m.pdf|PDF}}) \\
 5. Randomized/Expected Analysis  ({{:cs500e.ppt|PPT}}, {{:cs500e.pdf|PDF}}) \\ 5. Randomized/Expected Analysis  ({{:cs500e.ppt|PPT}}, {{:cs500e.pdf|PDF}}) \\
 3. Average-Case Analysis ({{:cs500c.ppt|PPT}}, {{:cs500c.pdf|PDF}}) \\ 3. Average-Case Analysis ({{:cs500c.ppt|PPT}}, {{:cs500c.pdf|PDF}}) \\
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 Instead use KLMS to reach out to the TAs (preferred), \\ Instead use KLMS to reach out to the TAs (preferred), \\
 or write to the dedicated email address [[cs500@theoryofcomputation.asia|cs500@theoryofcomputation.asia]]. or write to the dedicated email address [[cs500@theoryofcomputation.asia|cs500@theoryofcomputation.asia]].
 +