Knowledge Discovery and Machine Learning Patterns For Successful Mobile Applications


Machine learning and knowledge discovery techniques can be used for building mobile applications that perform well under new situations. Software developers can leverage the network effects of hundreds of thousands of users providing real-time, abundant training sets of usage data to build cost-effective predictive products and services.

  • Basics
    • Concepts
    • Knowledge representation
  • Algorithms
    • Rules
    • Decisions trees
    • Instance-based learning
    • Clustering
    • Evaluation
  • The tool set
    • System architectures
      • Mobile vs cloud separation of concerns
    • Programming languages
    • Structured vs unstructured data instrumentation
    • Toolkits
  • Case studies (samples, code, and diagrams)
    • Text summarization
    • Document and data search
    • Urban traffic flows real-time optimization
  • Q&A
雅虎资深首席工程师,Summly CTO

Eugene Ciurana是一位活跃在旧金山和莫斯科地区的开源贡献者、布道师、企业家。他专注于构建稳健又可扩展的敏捷开发,在诸如Summly、沃尔玛、AT&T、JP Morgan、Oracle、IBM等公司带领团队设计并开发高性能和大数据系统。同时他也在亚洲、东欧、或硅谷帮助很多创业公司打造他们的坚实技术基础。作为雅虎的资深首席工程署,他带领团队为下一代移动和Web应用设计并开发高扩展性语义化Web系统。Eugene经常用/nick pr3d4t0r的IRC代号活跃在#java, #python, #awk, #R, #iphonedev, 和#security等频道。