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留學(xué)監(jiān)理網(wǎng)
留學(xué)機(jī)構(gòu)監(jiān)理平臺(tái)
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開(kāi)始日期:
2023年7月15日
專(zhuān)業(yè)方向:
計(jì)算機(jī)與人工智能
導(dǎo)師:
Sorin(布朗大學(xué) Brown University 講席終身正教授)
課程周期:
2周專(zhuān)業(yè)預(yù)修+2周在線(xiàn)科研+2周線(xiàn)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(zhuān)業(yè)預(yù)修+2周在線(xiàn)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,導(dǎo)師將介紹用于知識(shí)發(fā)現(xiàn)的大數(shù)據(jù)挖掘的基礎(chǔ)編程模型和算法。學(xué)生對(duì)其代碼實(shí)現(xiàn)后,將使用真實(shí)生活中的數(shù)據(jù)集(如Yelp評(píng)論、亞馬遜交易和MovieLens數(shù)據(jù)等)進(jìn)行模型訓(xùn)練,并檢測(cè)出有意義的用戶(hù)偏好及習(xí)慣。在項(xiàng)目中后期,學(xué)生將結(jié)合所學(xué)知識(shí)及導(dǎo)師建議對(duì)基礎(chǔ)推薦算法及模型進(jìn)一步優(yōu)化研究,構(gòu)建一個(gè)新穎、準(zhǔn)確且高效的個(gè)性化推薦系統(tǒng),并在項(xiàng)目結(jié)束時(shí)提交項(xiàng)目報(bào)告、進(jìn)行成果展示。This program will introduce the fundamental programming models and algorithms used in mining Big Data for knowledge discovery. Specifically, the lecture will cover MapReduce, Frequent Itemset Mining, Clustering & Dimension Reduction, and Recommendation Systems. The assignments will include implementing algorithms introduced in the lecture to detect meaningful patterns from real datasets (e.g., Yelp reviews, Amazon transactions, and MovieLens data). At the end of the course, the students are expected to conduct a research project by combining the knowledge learned in class to build a novel recommendation system. 個(gè)性化研究課題參考 Suggested Research Fields 構(gòu)建基于內(nèi)容的電影推薦系統(tǒng) Content-based movie recommender 構(gòu)建基于協(xié)同過(guò)濾的推薦系統(tǒng) Building recommendation system based on collaborative filtering 構(gòu)建一個(gè)混合位置的餐廳推薦系統(tǒng) Building a hybrid recommendation system for location 數(shù)據(jù)挖掘其他應(yīng)用如:使用公開(kāi)數(shù)據(jù)進(jìn)行空氣質(zhì)量預(yù)測(cè)和預(yù)報(bào) Other applications of data mining, such as air quality prediction and forecasting using open data