方向：理工
专业：人工智能
适合人群：电子与计算机科学,电子与通信工程,软件工程,机器学习,计算机工程,计算机网络,人工智能,数据分析
是否可以加论文：是
项目时长及形式：英文
产出：
7周在线小组科研学习+5周论文辅导学习 共125课时
学术报告
优秀学员获主导师Reference Letter
EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等级别索引国际会议全文投递与发表（可用于申请）
结业证书
成绩单
项目介绍：
本项目将带领学生详细了解机器学习的主要方法和当前的研究方向，涵盖机器学习中的不同算法的分析与对比。项目在讨论至今仍有效的如决策树的经典算法外，还将讨论以深度学习为例的改变了机器学习领域的新技术。学生将在导师的指导下挑选研究问题，在项目结束时完成项目报告，进行成果展示。
This course is designed to give students a detailed overview of the key approaches and current research directions in machine learning. Using a fast-paced, but still detailed set of lectures, the course will cover the different challenges in machine learning, different algorithms to address these challenges, and a comparative analysis of different algorithms to allow one to pick the right algorithm for a given machine learning task. The discussion will include both “classic” algorithms that are still effective today (e.g., decision trees) as well as new techniques that have transformed the area of machine learning (e.g., deep learning). The lectures will be accompanied with a set of group-based project assignments that will provide students hands-on experience with different machine learning algorithms to solve important tasks. The project assignments will be replaced by reading or research assignments for students who do not have programming background. Finally, students will prepare a research report and presentation to be shared with the class.
个性化研究课题参考 Suggested Research Fields
欺骗性、重复性的广告检测算法研究 Research on Deceptive and Duplicate Advertisement Detection Algorithms
针对用户搜索记录的酒店推荐算法 Recommendation System for Hotel Reservations Based on the User’s search History
根据网约车当前运行轨迹，预测本次行程时间的算法开发 Predict the total travel time of taxi trips based on their initial partial trajectories
预测土壤的物理化学成分 Predict physical and chemical properties of soil using spectral measurements
更多留学问题欢迎咨询晨晟留学之家，电话(同微信) 18071732056