机器学习就是基于一些高度复杂的算法和技术，在一个非生命的物体、机器或系统中构建人类行为。机器学习在生活中的应 用很多，例如机器人根据搜寻自身环境的经验数据提供更好的导航服务，机器人根据目标病人的历史健康记录预测出哪种疗法治疗某种疾病极为有效，以及语音识别系统运用以往听你说话的经验更好地理解你的指令内容。机器学习是计算机科学以及信号信息领域中重要的热点研究领域。随着互联网、物联网等的快速发展，机器学习在多个领域（数据挖掘、大数据分析、视频技术、音频技术、智能机器人技术等）成为关键核心和职称技术。本课程讲授机器学习和数据分析的相关基础理论、主流机器学习思想和方法，旨在让大家深入了解从事机器学习以及相关学科应用的研究人员目前需要学习的方法、技术、数学和算法，为开展相关领域的技术开发和科学研究奠定基础。
大数据分析是指对规模巨大的数据进行分析。大数据可以概括为5个V， 数据量大(Volume)、速度快(Velocity)、类型多(Variety)、价值(Value)、真实性(Veracity) 。大数据作为时下火热的IT行业的词汇，随之而来的数据仓库、数据安全、数据分析、数据挖掘等等围绕大数据的商业价值的利用逐渐成为行业人士争相追捧的利润焦点。随着大数据时代的来临，大数据分析也应运而生。用于展现分析的前端开源工具有JasperSoft，Pentaho, Spagobi, Openi, Birt等等。用于展现分析商用分析工具有Style Intelligence、RapidMiner Radoop、Cognos, BO, Microsoft Power BI, Oracle,Microstrategy,QlikView、 Tableau 。国内的有BDP，国云数据(大数据魔镜)，思迈特，FineBI等等。
Machine learning is based on some highly complex algorithms and techniques to build human behavior in a non-living object, machine or system. Machine learning should use a lot in life, such as robot according to search their environment experience data to provide better navigation service, the history of robot according to the target patient health records to predict what kind of treatment of a disease is the most effective treatment method, and the speech recognition system using the past experience of listening to you to better understand your instruction. Machine learning is an important research field in computer science and signal information. With the rapid development of the Internet and the Internet of Things, machine learning has become a key core and title technology in many fields (data mining, big data analysis, video technology, audio technology, intelligent robotics technology, etc.). This course teaching of machine learning and data analysis of related basic theory, the mainstream of machine learning thought and method, aims to let everybody understand engaged in machine learning and related disciplines researchers now need to learn the method, technology, math and algorithm, in the related fields of technology development and lay the foundation of scientific research.
Big data analytics refers to the analysis of data on a huge scale. Big data can be summarized into five Vs: Volume, Velocity, Variety, Value and Veracity. Big data is the most popular word in the IT industry, followed by the use of data warehouse, data security, data analysis, data mining and so on around the business value of big data has gradually become the focus of profits sought after by industry people. With the advent of the era of big data, big data analysis comes into being. Front-end open source tools for presenting analysis include JasperSoft,
Pentaho, Spagobi, Openi, Birt, and so on. Commercial analytics tools for presenting analytics include Style Intelligence, RapidMiner, Radoop, Cognos, BO, Microsoft Power BI, Oracle, Microstrategy, QlikView, Tableau. Domestic BDP, National Cloud Data (Big data Magic mirror), Smart, FineBI and so on.