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requirements.txt 791 B

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  1. appnope==0.1.0
  2. backports.ssl-match-hostname==3.4.0.2
  3. certifi==2015.4.28
  4. decorator==4.0.2
  5. funcsigs==0.4
  6. functools32==3.2.3.post2
  7. gnureadline==6.3.3
  8. ipykernel==4.0.3
  9. ipython==4.0.0
  10. ipython-genutils==0.1.0
  11. ipywidgets==4.0.2
  12. Jinja2==2.8
  13. jsonschema==2.5.1
  14. jupyter==1.0.0
  15. jupyter-client==4.0.0
  16. jupyter-console==4.0.1
  17. jupyter-core==4.0.4
  18. MarkupSafe==0.23
  19. matplotlib==1.4.3
  20. mistune==0.7.1
  21. mock==1.3.0
  22. nbconvert==4.0.0
  23. nbformat==4.0.0
  24. nose==1.3.7
  25. notebook==4.0.4
  26. numpy==1.9.2
  27. path.py==8.1
  28. pbr==1.6.0
  29. pexpect==3.3
  30. pickleshare==0.5
  31. ptyprocess==0.5
  32. PyBrain==0.3
  33. Pygments==2.0.2
  34. pyparsing==2.0.3
  35. python-dateutil==2.4.2
  36. pytz==2015.4
  37. pyzmq==14.7.0
  38. qtconsole==4.0.1
  39. scikit-learn==0.16.1
  40. scipy==0.16.0
  41. simplegeneric==0.8.1
  42. six==1.9.0
  43. sklearn==0.0
  44. terminado==0.5
  45. tornado==4.2.1
  46. traitlets==4.0.0
  47. wheel==0.24.0

机器学习越来越多应用到飞行器、机器人等领域,其目的是利用计算机实现类似人类的智能,从而实现装备的智能化与无人化。本课程旨在引导学生掌握机器学习的基本知识、典型方法与技术,通过具体的应用案例激发学生对该学科的兴趣,鼓励学生能够从人工智能的角度来分析、解决飞行器、机器人所面临的问题和挑战。本课程主要内容包括Python编程基础,机器学习模型,无监督学习、监督学习、深度学习基础知识与实现,并学习如何利用机器学习解决实际问题,从而全面提升自我的《综合能力》。