获得教授的学术推荐信（教授用学校正式 edu 邮箱网推）
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）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不出国门跟随海外名校教授学习，节省国际旅费
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
新生指南、开营仪式、欢迎晚宴
01/31 Introduction and history of AI and machine learning.
Classification, Evaluation, Cross-validation.
人工智能和机器学习的介绍以及发展史。学习分类、评估、交叉验证
02/01 Nearest Neighbors, Naïve Bayes, Decision Trees, Random
Forests, Boosting, Support Vector Machines.
最近邻规则分类，朴素贝叶斯分类，决策树，随机森林，提升法，支持向量机
02/02 High dimensional data, Principal Component Analysis (PCA),
NIPALS, PCA via SVD, Multi-dimensional Scaling, Canonical Correlation Analysis.
高维数据，主成分分析，NIPALS 算法，主成分分析和奇异值分解，多维标度法，典型关联分析
02/03 K-means clustering. Vector Quantization. Expectation
Minimization (EM). Gaussian Mixture Models and EM.
k 均值聚类，矢量量化，最大期望算法，高斯混合模型和最大期望算法的关系
02/04 Linear and Logistic Regression. L1 Regularization. Kernel Regression. Markov Chains.
线性回归和逻辑回归， 正则化方法 L1，核回归，马尔可夫链
02/05 Hidden Markov Models. Conditional Random Fields. Graphical
Models. Variational Inference.
隐马尔可夫模型，条件随机场，图解模型，变分推断
02/06 Neural Networks. Convolutional Neural Networks. Recurrent
Neural Networks. Autoencoders.
神经网络，卷积神经网络，循环神经网络，自编码器
02/07 Reinforcement Learning. Deep Reinforcement Learning. Societal Impacts of AI. Recap.
强化学习， 深度强化学习， 人工智能的社会影响， 重新翻盘
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
Summary:
Topic: Artificial Intelligence
Professor: Rakesh Kumar
This course is designed to give students an in-depth knowledge of the key approaches and current research directions in applied artificial intelligence and machine learning. We start with a discussion of the history of AI and machine learning and its goals. We then rigorously define the classification problem and discuss in detail key classification algorithms. Subsequently, we provide an in-depth discussion of techniques to process high dimensional data. A deep dive into different clustering and regression techniques is next. Then, we delve into techniques based on hidden Markov models and Markov decision processes. Finally, we move to an in-depth discussion of neural networks, autoencoding, and reinforcement learning. The course ends with a recap and a discussion of the societal impact of AI.
The course requires ~2 year experience with coding and computer science, and basic knowledge of probability and statistics. Several concrete examples and assignments will be used throughout the course to refine students’ understanding. This will build a solid foundation for students considering Artificial Intelligence and Machine Learning as a career.
Day 1: Introduction and history of AI and machine learning. Classification, Evaluation, Cross-validation.
Day 2: Nearest Neighbors, Naïve Bayes, Decision Trees, Random Forests, Boosting, Support Vector Machines.
Day 3: High dimensional data, Principal Component Analysis (PCA), NIPALS, PCA via SVD, Multi-dimensional Scaling, Canonical Correlation Analysis.
Day 4: K-means clustering. Vector Quantization. Expectation Minimization (EM). Gaussian Mixture Models and EM.
Day 5: Linear and Logistic Regression. L1 Regularization. Kernel Regression. Markov Chains.
Day 6: Hidden Markov Models. Conditional Random Fields. Graphical Models. Variational Inference.
Day 7: Neural Networks. Convolutional Neural Networks. Recurrent Neural Networks.
Autoencoders.
Day 8: Reinforcement Learning. Deep Reinforcement Learning. Societal Impacts of AI. Recap.
