8月6日-10日,9:00-13:45,清华大学,北京
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        今天,如果你从事互联网搜索,在线广告,用户行为分析,图像识别,自然语言理解,或者生物信息学,智能机器人,金融预测,那么有一门核心课程你必须深入了解,那就是-机器学习(Machine Learning)。作为人工智能的核心内容,机器学习致力于开发智能的计算机算法从历史经验数据中学习出有用的模型,从而对未知数据或事件做预测。作为一门前沿学科,它结合了计算机算法,概率论,统计学,脑神经科学,控制论,心理学,和优化理论等多方面知识。

       两位授课者在机器学习领域享有国际声誉,不仅各自在世界顶级杂志和会议上发表了上百篇学术论文,而且都在著名高科技公司积累了多年左右的工作经验。通过这门课程,学生将系统掌握习机器学习的基本知识,理论,和算法,还将通过一些实例领略其在应用中发挥的巨大作用。

具体课程内容安排(pdf):

每天的日程为

09:00-09:45 第一节课

09:55-10:40 第二节课

10:50-11:35 第三节课

11:35-13:00 中午休息和交流

13:00-13:45 第四节课

讲课内容为:

 

Day 1

lecture 1: Introduction to ML and review of linear algebra, probability, statistics (kai)

lecture 2: linear model (tong)

lecture 3: overfitting and regularization (tong)

lecture 4: linear classification (kai)

Day 2

lecture 5: basis expansion and kernel methods (kai)

lecture 6: model selection and evaluation (kai)

lecture 7: model combination (tong)

lecture 8: boosting and bagging (tong)

Day 3

lecture 9: overview of learning theory (tong)

lecture 10: optimization in machine learning (tong)

lecture 11: online learning (tong)

lecture 12: sparsity models (tong)

Day 4

lecture 13: introduction to graphical models (kai)

lecture 14: structured learning (kai)

lecture 15: feature learning and deep learning (kai)

lecture 16: transfer learning and semi supervised learning (kai)

Day 5

lecture 17: matrix factorization and recommendations (kai)

lecture 18: learning on images (kai)

lecture 19: learning on the web (tong)

lecture 20: summary and road ahead (tong)