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開始日期:
2023年7月8日
專業(yè)方向:
計算機與人工智能
導師:
Miquel(哥倫比亞大學 Columbia University 教授)
課程周期:
2周專業(yè)預(yù)修+2周在線科研+2周線下面授
語言:
英文
建議學生年級:
大學生 高中生
項目產(chǎn)出:
2周專業(yè)預(yù)修+2周在線科研+2周深入面授科研與實驗室Workshop 與諾貝爾獎得主交流機會 學術(shù)報告 優(yōu)秀學員獲主導師Reference Letter EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等級別索引國際會議全文投遞與發(fā)表指導(共同一作或獨立一作可選) 結(jié)業(yè)證書 成績單
項目介紹:
項目中將重點探究機器學習中的經(jīng)典算法和深度學習中的神經(jīng)網(wǎng)絡(luò)的構(gòu)成,導師將結(jié)合相關(guān)理論,以金融數(shù)據(jù)的處理為例,類比股票預(yù)測小程序,帶領(lǐng)學生開發(fā)并優(yōu)化自己的算法小程序并完成項目報告,進行成果展示。在此過程中,你將了解到人工智能及機器學習算法的廣泛應(yīng)用及其給軟件工程帶來的無限可能性。 學生將進入到世界知名學府-哥倫比亞大學,在為期兩周的實地科研學習中與教授、Teaching Fellow面對面交流,在實驗室中將理論與實踐結(jié)合,沉浸式感受濃厚的學術(shù)氛圍。用餐在校內(nèi)食堂、住宿在學校宿舍中、生活在美麗、靜謐的校園內(nèi),學生將真正零距離體驗名校文化與生活方式。 With billions of mobile devices worldwide and the low cost of connected medical sensors, recording and transmitting financial data has become easier than ever. However, this ‘wealth’ of financial data has not yet been harnessed to provide actionable information. This is due to the lack of smart algorithms that can exploit the information encrypted within these ‘big databases’ of time series and take individual variability into account. Exploiting these data necessitates an in-depth understanding of the use of advanced digital signal processing and machine learning tools (e.g. deep learning) to recognize and extract characteristic patterns, and the ability to translate these patterns into actionable information. The creation of intelligent algorithms combined with existing and novel wearable and portable biosensors offers an unprecedented opportunity to monitor markets remotely and support the management of their condition. Data science to solve practical questions in this course you will learn about aspects of information processing including data preprocessing, visualization, regression, feature selection, classification (LR, SVM, NN), and their usage for decision support in the context of finance. The course aims to provide an overview of computer tools and machine learning techniques for dealing with financial datasets (time series). The course is practical with computer-based tutorials and assignments. The necessary theory will be covered. The lectures are divided as follows: ML basis, Popular classifiers, and Deep Learning.