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Lecture 1 System View.pptx - 已修復.pdf

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Lecture 1 System View.pptx - 已修復.pdf

  1. 1. 了解自然科學與社會科學的 系統與邏輯 NTUT HW Chiu Introduction to System and Logic in Human Intelligence and Nature Intelligence
  2. 2. System and View
  3. 3. Big data ? Big fear? Big Bang ?
  4. 4. Too big to see clearly
  5. 5. HI vs AI vs IT
  6. 6. IOT •Image/video /audio/text •Open source •Proprietary •Grabbed Big data • Artificial feature extraction • Statistics & Math • Visual presentation • Data Science • Storage & Processing Artificial intelligence • Logic • Fuzzy • ML • DL • xNN Management •Assistance •Control •Decision •Design Philosophy •Benefit •Regulation •Creativity Introduction to Artificial Intelligence (人工智慧概論) Data Science (資料科學) Data Mining (資料探勘) Big Data Analytics (大數據分析) Machine Learning (機器學習) Big Data Technology and Administration (大數據技術與管理) Deep Learning (深度學習) Evidence-Based Management 實證管理 Project Management 專案管理 Accounting & Finance 高階會計 國際金融 Decisions and Strategy策略與決策
  7. 7. • 1. System View: Life , logic/system thinking: Interdisciplinary and multidisciplinary • 1. Lab : Cloud setup , CMD ,CMD_function • 2. Scientific System • 2.1.Lab : Regex , • 3. System and signal/data/information : OO • 3.1.Lab : Python Data type • 4. System and Modeling • 4. Lab : Sympy, Scipy, ODE • 5. HI system : • 5. Lab : Numpy, Panda, Visualization • 6. Logic Calculus • 6 .Lab : Prolog, Python condition and function, Data Collection and Parsing, • 7. Linear algebra • 7. Lab : Linear regression & Signal processing / transformation and processing • 8. Statistics and probability and Implication, Sequential(Decision tree) • 8. Lab : Decision Tree of ski-learn examples • 9. Clustering, Classification, and strategy, Ensemble (Algorithm, game theory) • 9. Lab : Decision Tree of ski-learn examples
  8. 8. Crossover • Crossover of HI and IT in your brain
  9. 9. Summer vacation training course
  10. 10. Thinking models
  11. 11. System Thinking
  12. 12. What is this world?
  13. 13. What is meaning?
  14. 14. IT HI System Logic System View first
  15. 15. See the world • 看到 • 看出 • 看懂
  16. 16. View point • Insufficient information
  17. 17. Logic tools • Formal system(形式系統) • Language • Ambiguity • Completeness • Simplification • Uncomputable • Math • Accurate • Functionality • Simplification • Computable • 最大的差異是語言只能詮釋但數學可詮釋並且推論、演算、求解
  18. 18. 邏輯:如果你考60, 就不會被當。 定律:電子學必須修過,才能畢業。 模擬:根據你平時考,就知道你期末總分。 機率:如果你考50分,可能會被當。 模型:成績考核期中、期末各佔30%,平時作業佔40% 。 轉化:成績以考分開根號乘10來計分。 化簡:你考90分以上,就會得A 。 線性:作答時不要留白,多少都會給分。 技術:工數是這門課的先修。 維度:研究學入學考電子學及工數。 Different types of perspectives
  19. 19. 語言: while(ID[i]{“math”)+=10) 認知:我上課都很認真,所以我不會被當。 類別:總分以ABCD採計 矛盾:考100分,結果被當掉了。 回歸:全班的考試呈M型化分佈 應用:這科的作業把公式套進,就可以算出來了。 因果:你期末過關,是因為你期末考很好。 統計:你考分是中位數 謬論:跟老師不熟,分數越低。 預測:題目很難,所以我猜很多人被當。 Different types of perspectives
  20. 20. System and its theories
  21. 21. Boundary of systems Sciences Nature
  22. 22. Systems of science Science Empirical sciences Formal science Natural science Social science Foundation • Logic • Mathematics • Statistics • Physics • Chemistry • Biology Earth science • Astronomy • Economics • Political science Sociology • Psychology Application • Computer science • Engineering • Agricultural science Medicine • Dentistry • Pharmacy • Business administration • Jurisprudence • Education https://en.wikipedia.org/wiki/Branches_of_science 分類 容易理解
  23. 23. Business Administration / Management • Management science (MS) is the broad interdisciplinary study of problem solving and decision making in human organizations, with strong links to management, economics, business, engineering, management consulting, and other fields
  24. 24. What is system? • A system is a group of interacting or interrelated elements that act according to a set of rules to form a unified whole. • A system, surrounded and influenced by its environment, is described by its boundaries, structure and purpose and expressed in its functioning.
  25. 25. Close or open system ? • Boundary is beneficial for convergence.
  26. 26. internal feedback External feedback Environment
  27. 27. On the top of system view • 一覽無遺
  28. 28. Course Outline • Lecture 1: System view • Lecture 2: Scientific system • Lecture 3: Signal and system • Lecture 4: System and Modeling • Lecture 5: Human intelligence • Lecture 6: Logic Calculus • Lecture 7: Mathematics and Python for system S Nature S Science Material IT Social HI Complex AI
  29. 29. System thinking System Model Relation Unity
  30. 30. See the world Syste m Value Unity Relation Model Verification Balance Causality Function Upgrade HI 訊/雜 人事時地物 介系詞關係 認知 推論 真假/是非 預測 分析/認知 決策 看到 看懂 看好
  31. 31. Breadth and depth (深度與廣度) System Value Unity Relation Model Verification Balance Causality Function Upgrade Nature/ Science Re & Im Physical quantity Network Math Simulation Equilibrium Reasoning Operation Evolution HI 訊/雜 人事時地物 介系詞關係 認知 推論 真假/是非 預測 分析/認知 決策 Logic True/False Fact Rule Logic Check Probability Reasoning Implication Inference IT 1/0 Data Function OO Run Benchmark Condition Algorithm Optimization
  32. 32. Modeling Gaming System Value Unity Relation Model Verification Balance Causality Function Goal Science Re & Im Physical quantity Network Math Simulation Equilibrium Reasoning Operation Evolution HI 訊/雜 人事時地物 介系詞關係 認知 推論 真假/是非 預測 分析/認知 決策 Logic True/False Fact Rule Logic Check Probability Reasoning Implication Inference IT 1/0 Data Function OO Run Benchmark Condition Program Algorithm Modeling , Analysis, Gaming Analysis
  33. 33. Systematic view of Systems IT HI System Logic
  34. 34. System L1 Value Unity Relation Model Verification Balance Causality Function Goal Science L2 Re & Im Physical quantity Network Math Simulation Equilibrium Reasoning Operation Evolution HI L3 訊/雜 人事時地物 介系詞關係 認知 推論 真假/是非 預測 分析/認知 決策 Logic L4 True/False Fact Rule Logic Check Probability Reasoning Implication Inference IT L5 1/0 Data Function OO Run Benchmark Condition Program Algorithm Modeling Gaming Analysis
  35. 35. Find a view angle Recognize complicated as simple
  36. 36. Abstract thinking IOT Big data Artificial Intelligence Management Philosophy
  37. 37. How does system work? • Systems theory is the interdisciplinary study of systems
  38. 38. IOT •Image/video /audio/text •Open source •Proprietary •Grabbed Big data • Artificial feature extraction • Statistics & Math • Visual presentation • Data Science • Storage & Processing Artificial intelligence • Logic • Fuzzy • ML • DL • xNN Management •Assistance •Control •Decision •Design Linear Algebra Statistics Probability Gradient Descent Machine Learning Regression Decision Trees Natural Language Processing Recommender Systems
  39. 39. Applications • 單向1維時變訊息 • 語音辨識(蔡偉和、廖元甫、尤信程) • 生理訊號系統 (高立人) • 單向2維靜態 • 物:車(謝東儒、陳彥霖) • 單向N維時變訊息 • Transportation 、Manufacturing 、無人車 (劉建宏、陳彥霖、黃士嘉) • 影像、醫療影響辨識 ( 白敦文、張陽郎、賴冠廷) • 行為偵測 (高立人,曾柏軒) • 感測網路: (黃育賢、高立人、劉傳銘) • 商業 • 電商、消費行為、金融指標(王正豪、黃柏鈞) • 管理 • 最佳化:物流、通訊(曾恕銘) • 控制系統:電力、機械 (黃有評) • 決策系統:醫療(譚旦旭) • 排序:推薦系統 • 雙向Gaming : 下棋、客服 、 chat • 雙向循環 • 經濟循環 • 新聞熱點 訊 息 分 析 與 處 理 人 因 訊 息 分 析 與 管 理
  40. 40. Signal, Data, Information Time- Frequency Storable 確定性 & 不確定性 Information Data Signal Digital logistics Manage time-variant cognitive warfare
  41. 41. System and signal properties • Static / Dynamic • Linear / Nonlinear (LTI) • Time-invariant / Time-variant • Continuous(Analog) / Discrete (Digital) • In time ; in space ; in event • Scalar / Vector / Multi-Dimensional • Instantaneous / Dynamic • Causal / Noncausal • Reversible / Irreversible • Deterministic / Stochastic • Periodical / Random • adaptive system https://www.tutorialspoint.com/digital_signal_processing/
  42. 42. Modeling of Autonomous System
  43. 43. Science and Social System Sciences Nature • 物質運作的宇宙沒有計算,更不會算計。 • 人類文明精於計算,精於預計,平衡於算計
  44. 44. Human and Nature System Intelligence HI AI IT Atom 訊息 粒子/訊息 Unity 心/腦 物 System 人文 自然
  45. 45. Brain Thinking system HI 訊/雜 人事時地物 介系詞關係 認知 推論 真假/是非 預測 分析/認知 決策 Logic True/False Fact Rule Logic Check Probability Reasoning Implication Inference
  46. 46. Causality of sciences and systems • Most of scientific systems are found • Engineering systems are created.
  47. 47. Natural vs. Human-Made Systems • Races vs Racism • Marine ecosystem vs supply chain ecosystem • Human-Made system means the system is designed • Design indicates the advantages of administration, prediction , management and optimization.
  48. 48. • Tensorflow • pytorch ML • scikitlearn Modeling • Math: sympy • Data :Scipy Computing • matplotlib Data Visualization • Numpy • Pandas Data Management • Python • Ipython/Jupyter • Anaconda/conda Data tools 數 學 Math 數 據 Data np / array / matrix df plt number / string / list / dict ML, Regression Solve Optimization Neural Network, CNN, RNN Build model from data
  49. 49. List of available solutions Data Source Data Collection /Ingestion Stream Data Processing Data Storage Data Presentation /Analysis Communication Protocol Communication Data format: JSON, CSV, BSON, YAML, XML
  50. 50. System-Model-Data Data Collection Model Building System Operation

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