海外導(dǎo)師線下項(xiàng)目—2023暑期線下科研·海外：哥倫比亞大學(xué)教授計(jì)算機(jī)與人工智能科研課題

開(kāi)始日期：

2023年7月8日

專業(yè)方向：

計(jì)算機(jī)與人工智能

導(dǎo)師：

Miquel（哥倫比亞大學(xué) 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(yǔ)言：

英文

建議學(xué)生年級(jí)：

大學(xué)生 高中生

項(xiàng)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(shí)驗(yàn)室Workshop 與諾貝爾獎(jiǎng)得主交流機(jī)會(huì) 學(xué)術(shù)報(bào)告 優(yōu)秀學(xué)員獲主導(dǎo)師Reference Letter EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等級(jí)別索引國(guó)際會(huì)議全文投遞與發(fā)表指導(dǎo)（共同一作或獨(dú)立一作可選） 結(jié)業(yè)證書(shū) 成績(jī)單

項(xiàng)目介紹：

項(xiàng)目中將重點(diǎn)探究機(jī)器學(xué)習(xí)中的經(jīng)典算法和深度學(xué)習(xí)中的神經(jīng)網(wǎng)絡(luò)的構(gòu)成，導(dǎo)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(cè)小程序，帶領(lǐng)學(xué)生開(kāi)發(fā)并優(yōu)化自己的算法小程序并完成項(xiàng)目報(bào)告，進(jìn)行成果展示。在此過(guò)程中，你將了解到人工智能及機(jī)器學(xué)習(xí)算法的廣泛應(yīng)用及其給軟件工程帶來(lái)的無(wú)限可能性。 學(xué)生將進(jìn)入到世界知名學(xué)府-哥倫比亞大學(xué)，在為期兩周的實(shí)地科研學(xué)習(xí)中與教授、Teaching Fellow面對(duì)面交流，在實(shí)驗(yàn)室中將理論與實(shí)踐結(jié)合，沉浸式感受濃厚的學(xué)術(shù)氛圍。用餐在校內(nèi)食堂、住宿在學(xué)校宿舍中、生活在美麗、靜謐的校園內(nèi)，學(xué)生將真正零距離體驗(yàn)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.