Chapter 11 Final Notes

11.1 Topics We Covered

• Computer basics.

• Numerical linear algebra.

• Optimization.

• Smoothing.

• A little machine learning.

• Simulation.

• MCMC .

• A (very) little parallel computing.

11.2 Some Topics We Did Not Cover

• Bootstrap.

• Graphics and visualization.

• Data technologies.

• Reproducibility.

• Numerical integration.

• Symbolic computation.

• Data mining.

• Pattern recognition.

• Functional data analysis.

• Parallel computing for big data:

  • Distributed arrays, pbd packages.
  • MapReduce, distributed file systems, and Hadoop.
  • Distributed data frames and Spark. Some examples are available on line.

• Column-oriented file formats:

  • Parquet files.
  • Apache Arrow (arrow package).

• Machine learning platforms:

  • TensorFlow (tensorflow package).
  • Torch (torch for R).

• Docker, Rocker, and other virtualization technologies.

• Cloud computing (e.g. AWS, Azure).

• GitHub Actions.

11.3 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,