❶ 获得教授的学术推荐信（教授用学校正式 edu 邮箱网推）
❷ 系统科学的指导和训练学生进行学术文章写作和发表（国际 CPCI/EI 会议论文）
❸ 全英文语境展开项目，突破自我，助力 GT 考试、申请面试和未来学习
❹ 不出国门跟随海外名校教授学习，节省国际旅费
❺ 获得高质量英文个人科研报告，可在申请材料中提交
prof.Kumar
UIUC 电气和计算机工程终身教授
Hyperion Core, Inc 联合创始人&首席架构师荣获纳尔逊•曼德拉领导奖
荣获罗纳德•W•普拉特教师杰出教学奖
为主流媒体和杂志撰写科技、政策和社会交叉热点专栏
任职大学
伊利诺伊大学厄巴纳-香槟分校(University of Illinois at Urbana-Champaign)，建立于 1867 年，是一所享有世界声望的一流研究型大学。该校是美国“十大联盟(Big Ten)”创始成员，美国大学协会(AAU)成员，被誉为“公立常春藤”，也曾多年多次在世界大学学术排名位居前三十，校友中先后有 23 位获得诺贝尔奖，在美国公立大学中仅次于伯克利。很多学科素负盛名，工程学院始终位于美国大学工程院排名前五，计算机专业排名全美前五，仅次于卡耐基梅隆大学、麻省理工学院、斯坦福大学和加大伯克利分校。
Day Topics
01/30 Orientation, Opening Ceremony, Welcome Dinner
报到、开营仪式、欢迎晚宴
01/31 What is artificial intelligence? Have humans created real AI?
How can we test possible AI to decide? Applications.
什么是人工智能？人类创造了真正的人工智能吗？我们如何对一些可行的人工智能进行测试使其为我们做出决定？运用应用程序协助做出决定。
02/01 Machine learning basics: classification, regression, clustering.
机器学习的基础：分类，回归，聚类
02/02 What are key programming skills to explore artificial intelligence?
探索人工智能的关键编程技巧是什么？
02/03 How can we use linear regression to create machine learning？
如何使用线性回归来实现机器学习？
02/04 How can we use logistic regression to create machine learning?
如何使用逻辑回归来实现机器学习？
02/05 Decision Trees for machine learning.
机器学习的决策树
02/06 How can we create artificial neural networks
如何创建人工神经网络?
02/07 How could artificial intelligence impact our future?
人工智能如何影响人类的未来？
在地国际化特色
不出国门，就可以跟随海外名校教授进行学术研究，既为学生及其家庭节省了国际旅费又避免了时差等不适应性。面授课程设置在中国传媒大学校内进行，学术氛围好、校内设施便利且安全性强。
海外名校现职教授全程授课并指导科研
导师均为海外名校现职教授/终身教授，具有丰富的学术和科研经验，教授将根据中国学生特点设计课题，学术性与趣味性兼备，让学员在学术框架下，充分发挥想象力和创造力，从开题、文献检索、科研报告撰写、修改等多个环节进行学习，实现理论与实践的融会贯通。
专业 TA/RA 助教全程辅助
助教将经过严格筛选，择优录取相关专业 Master/PhD，并通过系统化培训及考核后参与进入科研项目组。助教将协助学生进行课题知识的预习和复习，并为学员提供辅助性指导和技术支持。此外，助教将与学生分享自身升学、海外学习和科研等经验，更加贴近学生实际情况，提供辅助性建议。
全面提升多项能力，提高名校录取几率
为来自全球高中生及大学生提供经济便捷的世界顶级学术研讨和实践的参与机会，帮助学 培养批判性思维、分析和创造性思维、复杂沟通-口头和书面表达和全球视野，通过深度学习，
帮助学生大幅提高申请几率，并向成为国际水平高精尖领域人才和高水平创新人才更近一步。
Syllabus
Topic: Artificial Intelligence and Machine Learning for Beginners
Professor: Rakesh Kumar
Summary:
Ever wondered what the hype about artificial intelligence and machine learning is all about? Can computers really make some decisions better than humans? What algorithms allow computers to make intelligent decisions? How can we program computers with these algorithms to enable artificial intelligence? If computers become intelligent, where does it leave the humans? Discover answers to these questions and much more in this introductory course on artificial intelligence and machine learning. We will first define the goals of artificial intelligence, ways to measure success, and discuss application areas where artificial intelligence has already shown promise. We will then discuss a set of concrete tasks we may want a computer to do that require intelligence. To enable these tasks, we need some programming skills - we will discuss python-based programming of artificial intelligence. We will then discuss a set of state-of-art algorithms (linear and logistic regression, decision trees, artificial neural networks) to enable artificial intelligence and machine learning. Finally, we will discuss broader societal issues (e.g., related to economy, ethics, policy, law, etc.), that arise due to the emergence of artificial intelligence.
The course does not require any experience with coding or computer science. An aptitude for science and math, and a ton of curiosity are all that is needed. Several assignments will be used throughout the course to refine students’ understanding. This understanding will build a solid foundation for students considering artificial intelligence and machine learning as a career.
