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統計學家教你穩紮穩打 R 語言資料分析已額滿

統計學家教你穩紮穩打 R 語言資料分析

資料科學是綜合統計學、資訊科學及領域知識的一門新興學科。因此,要成為一名資料科學家,對統計當然要有基本的認識。舉凡日常的機率統計問題,例如:算機率值,統計參數估計,假設檢定及模型配適等等,除了需要理論証明推導之外,也需以程式做數值的模擬與驗証。而 R 語言正是研究者分析資料及執行統計模擬不可或缺的工具。也因為 R 軟體具有豐富的統計套件,也成為資料科學領域裡最受歡迎的程式語言之一。本課程由統計學家教您用 R 進行資料處理、呈現及統計分析。從第一天 R 的基礎 + 進階 (資料輸出輸入及處理、統計圖形、機率分佈、假設檢定、變異數分析、模擬統計模型與迴歸分析) 到第二天的統計分析方法應用(探索式資料分析、資料探勘案例研究),除了著重在方法理論的講述、假設及觀念的闡釋,同時也強調實際 R 程式撰寫操作,希望藉由本課程之訓練,奠定學員 R 程式撰寫及資料統計分析的基礎,並具有判斷不同狀況該用什麼不同的統計方法的數據能力。  

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今天開始打造人工智慧團隊-給企業主及團隊領導者的經驗談,解析所有你將遇到的已發生,未發生,將發生的問題及陷阱已額滿

今天開始打造人工智慧團隊-給企業主及團隊領導者的經驗談,解析所有你將遇到的已發生,未發生,將發生的問題及陷阱

今天開始打造人工智慧團隊-給企業主及團隊領導者的經驗談,解析所有你將遇到的已發生,未發生,將發生的問題及陷阱最近的科技新聞熱點在人工智慧,街頭巷口人人都在談人工智慧,因為已證明在許多產業應用上的價值,讓許多企業趨之若騖,不論本來談不談/做不做資料科學及大數據研發及導入,今年開始都開始談人工智慧。他們可能說,我們已從大數據階段「進化」到人工智慧,比其它還在做大數據的公司還「先進」,這是種有趣的說法。事實上,大數據、機器學習及人工智慧是不可分割的。大數據是資訊來源,機器學習是資訊的處理方法,藉以淬取出複雜的規則,讓電腦展現出擬似人類智慧的行為,我們稱為人工智慧。換句話說,以今天的技術來說,大數據及機器學習是發展人工智慧的必要條件;在人工智慧技術領先的企業,同時也必然是大數據及機器學習的領先者,沒有人能跳過這兩者而直接開發出先進的人工智慧系統。因此,若想要導入人工智慧,過去欠的技術債,今天必須先還清。企業裡的資料基礎建設 (例如資料倉儲) 建好了嗎?資料管理有個統一的授權模式嗎?或是依舊得讓資料使用者一一去說服企業中分散各處的資料擁有者?資料的收集及產出有融入企業流程中嗎?還是每次得為了特定的專案再花人力時間資源來整理收集?資料應用的導入是預設得進行的常規任務,還是得另外花力氣去說服所有相關單位?分析資料的團隊是管理資料的團隊兼任,還是有個專業的資料分析團隊來進行?同時,所有的資料應用者都熟悉機器學習嗎?還是以傳統繪圖製表的方式在做基礎層次的資料視覺化?更直白的說法是,千萬不要以為公司有資料倉儲,打開電腦就有報表可以看,公司同仁開會討論時都是基於漂亮的即時圖表,就是把大數據應用的很好的公司。事實可能剛好相反,最淺層的資料應用是讓老板每天看圖表;最最深入的資料應用應用是隱形的,看不到圖表。不然你覺得 AlphaGo 是看著一堆圖表學到如何下棋的嗎?透過機器學習從資料淬取規則出來,而那些規則因為過於複雜,所以根本無法完整地視覺化呈現,只能以人工智慧系統來直接應用這些規則,讓系統根據當前的輸入值提供最佳的輸出值。過去幾年,我們在資料的蒐集及解讀上很熱衷,對於機器學習這種以看不見圖表的方式來分析資料及發展人工智慧應用有點後知後覺。十分期待本土各種產業在覺知後的奮起直追,特別在我們有特殊利基點的產業(如製造業、農業、生醫產業等)及社會領域(施政、公用事業、教育等)上,以真正踏實的人工智慧發展來改善社會及產業體質。在此課程中,我將為聽眾闡明資料科學、大數據、人工智慧、機器學習/深度學習等相近但又不同的詞彙,再以各領域的實際案例來分享資料的可能應用及實用價值。同時,我將與聽眾分享其協助多家企業培訓資料科學家及導入資料科學/人工智慧團隊的各種經驗,各種常見的問題以及如何解決這些問題的有效方式。如何選才/育才,才能讓團隊擁有最堅實的戰力以高效率來解決問題。如何選題,才能快速展現團隊的價值等等。最後,我將探討台灣發展人工智慧的挑戰及契機,以及企業應用人工智慧進行競爭力升級的方式及各種關鍵議題。

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使用 Python 來做機器學習初探已額滿

使用 Python 來做機器學習初探

Python 程式語言起步走:使用 Python 來做機器學習初探對於 Python 躍躍欲試嗎? 此課程將帶領對 Python 感到陌生卻又充滿興趣的您,手把手快速入門學會 Python 語言程式設計要點,並進行實際資料處理與機器學習,從基本的程式開發環境、資料型態與語法、到實際的資料整理並進行初步的機器學習應用。此課程主要設計給想學習 Python 程式設計並利用相關機器學習套件進行機器學習的夥伴們。希望在課程結束後,您能夠更熟悉 Python 語言這個程式工具,將來可以利用豐富方便的套件進行更深入之資料分析與機器學習應用。對於資料科學與 Python 程式設計有興趣,卻苦無入門機會的夥伴們,讓我們一起從無到有動手用 Python 來解決實務的資料問題。希望以深入淺出的課程講解與手把手的零基礎程式實作教學,帶給大家在未來機器學習進階應用的基礎。 

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深入淺出深度學習 (Dive into Deep Learning)已額滿

深入淺出深度學習 (Dive into Deep Learning)

深入淺出深度學習 (Dive into Deep Learning)深度學習 (Deep Learning) 是機器學習 (Machine Learning) 中近年來備受重視的一支,深度學習根源於類神經網路 (Artificial Neural Network) 模型,但今日深度學習的技術和它的前身已截然不同,目前最好的語音辨識和影像辨識系統都是以深度學習技術來完成。 你可能在很多不同的場合聽過各種用深度學習做出的驚人應用 (例如:最近紅遍大街小巷的 AlphaGo),聽完以後覺得心癢癢的,想要趕快使用這項強大的技術,卻不知要從何下手學習,以及不知道要如何應用深度學習於各式應用上,那這門課就是你所需要的。這門課程將由臺大資訊系陳縕儂教授利用短短的一天議程簡介機器學習及深度學習,包含深度學習基礎理論、模型各種變形、及深度學習應用的發展及趨勢。本課程希望幫助大家不只能了解深度學習,也可以了解如何應用深度學習於實際的應用問題上。無論是從未嘗試過深度學習的新手,還是已經有一點經驗想更深入了解,都可以在這門課中有所收穫。本課程由三個部分組成:
  1. 什麼是機器學習及深度學習 這堂課會簡介機器學習領域中不同的方法,以監督式學習 (Supervised Learning) 為主,並介紹機器學習及深度學習技術中包含的三個主要步驟:1) 設定模型架構、2) 定義目標函數、3) 選擇最好的函數。
  2. 深度學習模型之變形及應用 這堂課要講深度學習模型兩個常見的變形:捲積式類神經網路 (Convolutional Neural Network, CNN) 不只常用在影像辨識上,應用在圍棋上也成果驚人;循環式類神經網路 (Recurrent Neural Network, RNN) 讓深度學習模型有了記憶力,可以處理更複雜的問題。
  3. 深度學習發展及趨勢 – 非監督式學習 & 強化式學習 深度學習在監督式學習領域已經有較成熟的發展,但是在機器學習中其他領域都才剛起步。這堂課會介紹近期深度學習的最新趨勢,以及深度學習的研究者們近期在意的重要問題。主要介紹並討論非監督式學習 (Unsupervised Learning) 以及強化式學習 (Reinforcement Learning) 領域中深度學習的應用及其效果,並討論深度學習未來之發展。
 

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製造資料科學:從預測性思維到處方性決策已額滿

製造資料科學:從預測性思維到處方性決策

資料科學 (Data Science) 與工業 4.0 (Industry 4.0) 是近幾年來廣為討論的主題,本課程以製造現場為實證對象,從資料的視角來尋求改善的契機。在複雜的製造現場環境裡,存在著各式各樣的議題,諸如品質、成本、交期、創新、彈性等,皆需持續改善以提升公司核心競爭力。事實上,製造業在台灣經濟發展上也扮演著舉足輕重的角色,在製造業的轉型過程中,如何以資料科學的角度,整合自動化實務和管理經驗,導入方法論以累積製造智慧 (Manufacturing Intelligence),相信是這個世代關注的焦點之一。 這門課程由國立成功大學資訊系暨製造所李家岩副教授主講,希望以深入淺出的方式,對製造資料科學作一整體性的介紹。課程專注於「問題本質的探索與觀念的釐清」,並輔以案例介紹工程資料分析時會遭遇的困難與挑戰。此外,對於機器學習或資料探勘強調的預測性分析 (Predictive Analytics),課程更進一步地延伸到處方性分析 (Prescriptive Analytics),以連結到管理者視角下,風險評估與決策制定的過程。希望課程內容能引起大家的興趣,並帶給大家在未來繼續學習進階知識的基礎。

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智慧城市中的時空大數據應用已額滿

智慧城市中的時空大數據應用

伴隨著城市化的腳步越來越快與城市人口日漸增加,提供城市居民必要的服務如交通、醫療、能源、環境、教育與公共安全等設施與都市規劃,受到龐大的壓力挑戰,錯誤的政策將導致大量的資源浪費與民怨,城市居民需要生活品質、便利設施、健康環境、便民政策、合理房價與更多社會福利等各種面向的公共需求。 由於資訊技術與行動裝置的快速發展,雲端運算、物聯網、感測網路、行動通訊、社交媒體、地理資訊等技術與服務日漸成熟,城市中由不同平台產生的異質性資料 (如感測網路、社群媒體、交通資訊、人群移動、地理特性與環境監測等資料) 已成為解決城市發展課題的重要手段,分析城市中的時空大數據將有利於開發者提供各種個人化或客製化的智慧型服務,也可輔助政府或企業管理高層做出聰明的決策,在本課程中將介紹如何處理城市的異質性大數據以及善用其開發社群媒體、商業策略、交通、能源、環保、旅遊與都市規劃等相關的智慧應用。
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統計學家教你穩紮穩打 R 語言資料分析已額滿

統計學家教你穩紮穩打 R 語言資料分析

統計學家教你穩紮穩打 R 語言資料分析資料科學是綜合統計學、資訊科學及領域知識的一門新興學科。因此,要成為一名資料科學家,對統計當然要有基本的認識。舉凡日常的機率統計問題,例如:算機率值,統計參數估計,假設檢定及模型配適等等,除了需要理論証明推導之外,也需以程式做數值的模擬與驗証。而 R 語言正是研究者分析資料及執行統計模擬不可或缺的工具。也因為 R 軟體具有豐富的統計套件,也成為資料科學領域裡最受歡迎的程式語言之一。本課程由統計學家教您用 R 進行資料處理、呈現及統計分析。從第一天 R 的基礎 + 進階 (資料輸出輸入及處理、統計圖形、機率分佈、假設檢定、變異數分析、模擬統計模型與迴歸分析) 到第二天的統計分析方法應用(探索式資料分析、資料探勘案例研究),除了著重在方法理論的講述、假設及觀念的闡釋,同時也強調實際 R 程式撰寫操作,希望藉由本課程之訓練,奠定學員 R 程式撰寫及資料統計分析的基礎,並具有判斷不同狀況該用什麼不同的統計方法的數據能力。 

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人類行為大數據分析:資料科學如何應用在教育及醫療領域已額滿

人類行為大數據分析:資料科學如何應用在教育及醫療領域

人類本身具有透過觀察另一人的行為以做出高階層判斷,如情緒、個性、喜好、意圖…等訊息的高度整合能力。這種人類的主觀感知機制不僅發生在日常生活中,更早已廣泛在行為科學 (behavior science) 領域中被應用於專業主觀評量與行為分析。近年來,隨著工程訊號處理演算法的進步,學科領域界線的破除,加上大數據資料科學的發展,慢慢匯流出新興跨學科整合式「人類行為訊號處理」:建置高可信度 (reliability)、可複製性 (repeatable) 及可擴展性 (scalable) 的系統將此人類的主觀感知機制自動化。 本課程將以數個教育及醫療應用實例為主軸,包含教育演講自動評分建置、急診檢傷疼痛指數系統開發、至臨床自閉症類群症狀行為診斷分析等等,貫穿並拆解「人類行為訊號處理」四大簡易步驟。希望透過本課程提供的簡易上手指南後,學員可以開始將各自領域手邊目前依人類的「主觀」認知而形成的專業判斷依據納入全新以資料角度而產生的「客觀」人類行為分析之決策工具。
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