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   "source": [
    "# Machine Learning and Statistics for Physicists"
   ]
  },
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   "metadata": {},
   "source": [
    "Material for a [UC Irvine](https://uci.edu/) course offered by the [Department of Physics and Astronomy](https://www.physics.uci.edu/).\n",
    "\n",
    "Content is maintained on [github](github.com/dkirkby/MachineLearningStatistics) and distributed under a [BSD3 license](https://opensource.org/licenses/BSD-3-Clause).\n",
    "\n",
    "##### ► [View course table of contents](Contents.ipynb)"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "This course assumes a basic familiarity with the core python language. If you are rusty or still learning, I recommend:\n",
    "- *A Whirlwind Tour of Python*, Jake VanderPlas: [free PDF](http://www.oreilly.com/programming/free/files/a-whirlwind-tour-of-python.pdf), [notebooks online](http://nbviewer.jupyter.org/github/jakevdp/WhirlwindTourOfPython/blob/master/Index.ipynb).\n",
    "\n",
    "We also assume a solid foundation of linear algebra, at this level:\n",
    "- *Linear Algebra Review and Reference* from Stanford CS229: [free PDF](http://cs229.stanford.edu/section/cs229-linalg.pdf)."
   ]
  },
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   "metadata": {},
   "source": [
    "## Recommended Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are some general textbook-style references that are most directly relevant to this course:\n",
    "- *Python Data Science Handbook*, Jake VanderPlas: [read online](https://jakevdp.github.io/PythonDataScienceHandbook/index.html), [buy a hardcopy](http://shop.oreilly.com/product/0636920034919.do).\n",
    "- *Statistics, Data Mining, and Machine Learning in Astronomy*, Zeljko Ivezic et al: [buy](https://www.amazon.com/Statistics-Mining-Machine-Learning-Astronomy/dp/0691151687).\n",
    "- *Think Bayes*, Allen Downey: [free PDF](http://greenteapress.com/wp/think-bayes/), [buy](https://www.amazon.com/gp/product/1449370780).\n",
    "\n",
    "There are many additional references for specific topics in the course notebooks."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are some additional references that I have found useful but which go somewhat beyond the scope of this course:\n",
    "- *Information Theory, Inference, and Learning Algorithms*, David MacKay: [free PDF](http://www.inference.org.uk/itprnn/book.pdf), [buy](https://www.amazon.com/dp/0521642981).\n",
    "- *Elements of Statistical Learning*, Trevor Hastie et al: [free PDF](https://web.stanford.edu/~hastie/Papers/ESLII.pdf), [buy](https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576).\n",
    "- *Deep Learning*, Ian Goodfellow et al: [free PDF](http://www.deeplearningbook.org/), [buy](https://www.amazon.com/dp/0262035618).\n",
    "- [Lecture notes](http://cs229.stanford.edu/syllabus.html) from [Stanford Machine Learning course CS229](http://cs229.stanford.edu/).\n",
    "- fast.ai online courses:\n",
    "  - [machine learning for coders](https://course.fast.ai/ml)\n",
    "  - [practical deep learning for coders](https://course.fast.ai/)\n",
    "  - [cutting edge deep learning for coders](https://course.fast.ai/part2.html)"
   ]
  }
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