Final Notes

class: center, middle, title-slide

.title[
# Final Notes
]
.author[
### Luke Tierney
]
.institute[
### University of Iowa
]
.date[
### 2023-05-19
]

---

## Topics We Covered

* Computer basics.

* Numerical linear algebra.

* Optimization.

* Smoothing.

* A little machine learning.

* Simulation.

* MCMC.

* A (very) little parallel computing.

---
layout: true
## Some Topics We Did Not Cover
---

* Bootstrap.

* Graphics and visualization.

* Data technologies.

* Reproducibility.

* Numerical integration.

* Symbolic computation.

* Data mining.

* Pattern recognition.

* [Functional data
  analysis](https://en.wikipedia.org/wiki/Functional_data_analysis).

---

* Parallel computing for big data:

* Distributed arrays, `pbd` packages.
    * [MapReduce](https://en.wikipedia.org/wiki/MapReduce),
      distributed file systems, and
      [Hadoop](https://hadoop.apache.org/).
    * Distributed data frames and
      [Spark](https://spark.apache.org/). Some examples are available
      [on line](../examples/spark.Rmd).

* Column-oriented file formats:

* [Parquet files](https://parquet.apache.org/).
    * [Apache Arrow](https://arrow.apache.org/) ([`arrow`
      package](https://arrow.apache.org/docs/r/)).

* Machine learning platforms:

* [TensorFlow](https://www.tensorflow.org/) ([`tensorflow`
      package](https://tensorflow.rstudio.com/)).
    * [Torch](http://torch.ch/) ([`torch` for
      R](https://torch.mlverse.org/)).

* [Docker](https://www.docker.com/),
  [Rocker](https://www.rocker-project.org/), and other virtualization
  technologies.

* [Cloud computing](https://en.wikipedia.org/wiki/Cloud_computing)
  (e.g. [AWS](https://aws.amazon.com/),
  [Azure](https://azure.microsoft.com/en-us/)).

* [GitHub Actions](https://docs.github.com/en/actions).

---
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## Homework and Project

Some of the things you got from these:

* Practice writing non-trivial programs that work.

* Appreciation of basic numerical issues.

* Experience using the computer to explore ideas.

* A better understanding of R.

* Experience in understanding and extending a statistical analysis
  framework

* Practice in conducting simulation experiments.

* A deeper understanding of some topic of your choice,

//adapted from Emi Tanaka's gist at //https://gist.github.com/emitanaka/eaa258bb8471c041797ff377704c8505