Intro to CS Course Outline

Transcript

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2. $POUFOUT Remorse 學⽣生(我)需要的是什麼？ 頂尖學校的課程分析 課程內容規劃 進⾏行/評量⽅方式
3. 剎竤剤涸匌銯 計概課 演算法課 C++語法課 PHP+MySQL
4. 怵皿岁铭 競賽[知識]資源豐富：校隊、TIOJ、HOJ、IOICamp(NTU CSIE)、算法竞赛⼊入⻔门经典：训练指南、挑戰程序設計競賽、I to A 有多少⼈人未來會⾛走競賽？(TOI 2! 10⼈人) 競賽培訓過程與普通教學不相同 我們教演算法⽐比選⼿手/教授教得好嗎？ 演算法是必須教授的主題 如何在深度與實⽤用中平衡
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7. For me, it’s a bad idea to… No offense
8. DON’T Teach Scratch, App Inventor, PHP +MySQL… Consider Math irrelevant Confine in a fixed set of knowledge Give up the chance to change
9. 갥㼺㷸吥涸铭玑 NTUEE (C++) http://ocw.aca.ntu.edu.tw/ntu-ocw/index.php/ocw/ cou/101S210/10 Stanford CS101 (Javascript) https://www.coursera.org/course/cs101 MIT 6.00 (Python) https://www.edx.org/course/introduction-computer- science-mitx-6-00-1x7
10. Ⰹ㺂 NTUEE (21) MIT (24) Stanford 1 Data Storage (Binary, Data Type, File Format, Huffman Coding) Data Types, Operators, Variables Branching, Branching, Conditionals Variables, Digital Image 2 Data Manipulation (Machine Language, Parallel) Iterative Programs, Functions, Recursion Loops, Expressions, Puzzles, Grayscale 3 Operating System (Kernel, Linux, Deadlock, Security) Floating Point, Finding Roots, Bisection, Newton/ Raphson If Logic, Blue Screen Effect, Bits Bytes 4 Networking/Internet (Topology, TCP/IP, RSA) List, Dictionaries Software(Programming Language), Networking, TCP/IP, CSV Operation 5 Algorithms (Shortest Path, Sorting, Time Complexity) Complexity(Linear, Quadratic, Exponential Algorithms) Binary Search, Sorting, D&C Strings, Boolean, Counting, Analog vs Digital
11. Ⰹ㺂
    con’d NTUEE MIT Stanford 6 Language (OOP, Encapsulation Prolog) Exception, Testing, Debugging Digital Media, Security 7 Data Abstraction (DS, STL, BST, Database) Dynamic Programming(Knapsack, Overlapping Subproblems, Optimal Substructure) 8 Graphics, AI (Search, Pruning, Genetic Algorithm) OOP(Class, Methods, Encapsulation, Inheritance) 9 Computational Theory Distributions, Monte Carlo (Estimating Pi), Curve Fitting, Stuck Market Simulation 䑞岌➝稲㢵⦐걆㚖 䩟㻜涸玑䒭鏤鎙怵皿岁铭玑 矦㋲剤馱涸眕⢿绢⡲
12. 铭玑Ⰹ㺂鋊ⷔ 資訊科學概論 專題演講 10次，每次2節 程式設計 實作課程 20次，每次2節 數學理論課程 6次，每次2節
13. 须鎝猰㷸嚌锸 Computer Architecture Operating Systems Programming Languages Computer Networks Algorithms and Theory of Computation Security and Cryptography Information Theory and Data Compression Computer Graphics/Vision Data Mining and Machine Learning Human-Computer Interaction Robotics
14. 须鎝猰㷸嚌锸 為什麼要安排這樣的課程？ Discovering Interets Helpful for further learning 為什麼需要專家學者演講？ Learning from an expert Cutting-edge research 如果要這麼做必須盡快張羅資源!
15. 玑䒭鏤鎙 Installation, Program, Binary Variable, Expression, Logic Loop, Function, Recursion Testing and Debugging List and Dictionary Tutorial: Command Line and Git
16. 玑䒭鏤鎙 Time Complexity & Sorting Queue & Stack / DFS & BFS Dynamic Programming, Shortest Path HTTP and HTML/CSS/JS Tutorial: Numpy & Pylab Build a Crawler
17. 玑䒭鏤鎙 OOP-1 Class and Methods OOP-2 Inheritance and Shadowing BST and DB Tutorial: Modern Web Development Build a Website with Tornado
18. 侸㷸椚锸 微積分 Optimization, Simulation… 線性代數 Everywhere, SVD, Graph, ML, CG … 機率統計 Almost Everywhere
19. Ꟛ佞铭玑 提供給全國⾼高中⽣生學習 發揮課程最⼤大的價值 推動資訊教學內容更新 …Why not?
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21. 鐱ꆀ倰䒭 Please, no tests (Including 上機考) 完成程式作業 (3~5 tasks/week) 完成學⽉月專案 (互評+評審) 演講筆記加分
22. 8IZ
    1ZUIPO Easy Syntax, Complete Library Dynamic but Static Typed Tornado, NumPy, SciPy, Cython, scikit- learn, NLTK, Theano … Various Resources codeforces.com , checkio.org
23. 䱺♴⢵ヤ 完成Coursera/edX/Udacity上⼀一項課程 (7~16週) 總課程數<5，每4~6⼈人組成Study Group 訂定每週研討時間(Combined with Entresoft) 繳交共同筆記 專題研究/專案製作 (3~10週) 應⽤用所學專業課程 訂定主題、研究⽅方向、分⼯工、時程 定期回報進度 尋找業界專家/學者協助 期末展覽+評審評分
24. 䲀讄铭玑 Web Development, Udacity (7週) Developing Android Apps, Udacity (7週) HTML5 Game Development, Udacity (9週) Intro to Data Science, Intro to Machine Learning, Udacity (10週) Interactive 3D Graphics, Udacity (10週) Machine Learning, Stanford University (11週) Image and Video Processing, Duke University (9週) Natural Language Processing, Columbia University (10週) Artificial Intelligence, UC Berkeley (12週) Cryptography I&II, Stanford University (12週) Data Mining, UIUC (16週) Data Science, Johns Hopkins University 機器學習基⽯石、機器學習技術 臺灣⼤大學 (16週)
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