Course Intro or "課程概述" in Chinese (Note that we minimize the use of non-English on this course website except here) :
天下雜誌的矽谷Stanford分析，Stanford的成功奠基於9字箴言：“跨領域，實境學，動手做”，也就是我常說的，Stanford教我的“跨領域，玩真的，做中學”，在Stanford有 a+b, b+c, c+d, ... 各種跨領域的課，而且強調動手，而不是萬年講義，講妖豔的理論公式，玩假papers。但台灣小確幸，跨領域在台灣往往被念，吃力不討好。我們只能不管負能量，急起直追：這門課是 b+c：Big data and Compilers. 蓋最早Big data的Map Reduce為Google的compiler高手發明。Map Reduce的人又跑去做Pregel。之後的Flume，Pregel之前的Sawzall語言皆為Compiler高手發明，這些都深深影響了Big Data領域。例如2014年Flume在Google甚至有取代Map Reduce之勢。
－ 週一1:30-2:20，5:30-6:20pm 講 Big data （“B”）。
－ 週一6:30pm講 Compilers（“C”）。
B組 focuses on Big Data processing needs, analytics, machine-learning and recommendation systems. C組 emphasizes on compilers and their contexts, be it Android compilation or Big Data languages. This is crucial especially today: Benefitting from Moore's Law, the main abstraction level in Computer Science has shifted higher rapidly. (In comparison, Taiwan's industry has been buried in the hardware, drivers, and benchmarking game.) Both "B" and "C" are taught by an author of Big Explorer, Android Virtual Machine and RenderScript Engine (Google).
Course Goal or "課程目標":
讓修課同學：從實際有影響力的系統開始，培養同學掌握基本能力及掌握Big Data的真功夫。
學生亟需這種跨領域的訓練。
Professor: 廖世偉 (SW Liao)
Teaching assistants: 夏誌陽, Li-Yuan Hung, Chi-Wei Lee，楊傑勛，林冠宇，劉建旻，洪任諭
Email: andBigdata2015@csie.ntu.edu.tw
Text Book, Reference Book
Compilers: Principles, Techniques, and Tools (2nd edition), by Aho, Lam, Sethi, Ullman
Grade (Note that Homework and project submissions are done via https://ceiba.ntu.edu.tw)
Hackathon：15%
including project proposal report and progress review.
Homework：25%
Midterm：25%
Final project：35%