# Teaching

##### Course materials and students mentored

### Course materials

- Spring 2021, CS470/570, Artificial Intelligence. my course materials
- Fall 2020, CS499/599, Unsupervised Learning. my course materials
- Summer 2020, Introduction to Machine Learning and Neural Networks, 90 minute lecture for summer school on “New Advances in Land Carbon Cycle Modeling” (3rd year). my course materials
- Spring 2020, CS499-3, Deep Learning. my course materials
- Fall 2019, CS599-6 / EE599-4, Machine learning research. my course materials
- Spring 2019, CS499-007, Machine learning algorithms. my course materials
- Fall 2018, Open source software engineering class at NAU. my course materials
- Summer 2017, International useR 2017 conference, Brussels, Belgium, Tutorial on Introduction to Supervised changepoint detection, my course materials, video.
- Spring 2017, Université de Montréal, Criminology Department, Introduction to R for Criminology, my course materials.
- Summer 2016, International useR 2016 conference, Stanford, CA, Tutorial on Understanding and Creating Interactive Graphics, my course materials.
- Spring 2011, Mines ParisTech, teaching assistant for Fabien Moutarde’s Machine Learning class, my course materials.

### Screencasts

Topics without links below are on my TODO list.

- Convolutional neural networks in R
- Number of hidden units is a regularization parameter (R keras)
- Neural networks using keras in R
- Make an R package with C++ code
- Machine learning basics in R
- Regular expressions for text parsing and data reshaping
- R and Emacs Speaks Statistics
- installation via package (ess, polymode)
- org-mode basics (links, html preview)

- Cluster computing on Monsoon
- Interpreting machine learning models with feature selection
- Data manipulation with data.table
- fread
- basic selections with first arg
- joins with first arg
- selecting columns with second arg
- summarize with second arg
- summarize by group with second arg
- foverlaps

- Static data visualization with ggplot2
- work thru examples in animint2 manual chapter 2

- Interactive data visualization with animint2
- git/github

### NAU students mentored

See my Lab Members web page.

### Google Summer of Code students mentored

I have mentored the following students in coding free/open-source software.

- Sanchit Saini, 2020, rtracklayer R package improvements.
- Himanshu Singh, 2020, animint2: interactive grammar of graphics.
- Julian Stanley, 2020, Graphical User Interface for gfpop R package.
- Anirban Chetia, 2020, testComplexity R package.
- Anuraag Srivastava, 2019, Optimal Partitioning algorithm and opart R package.
- Avinash Barnwal, 2019, AFT and Binomial loss functions for xgboost.
- Aditya Sam, 2019, Elastic net regularized interval regression and iregnet R package.
- Alan Williams, 2018, SegAnnDB: machine learning system for DNA copy number analysis, blog.
- Vivek Kumar, 2018, animint2: interactive grammar of graphics, blog.
- Johan Larsson, 2018, sgdnet: SAGA algorithm for sparse linear models.
- Marlin Na, 2017, TnT: interactive genome browser.
- Rover Van, 2017, iregnet: regularized interval regression.
- Abhishek Shrivastava, 2016, SegAnnDB: interactive system for labeling and machine learning in genomic data.
- Faizan Khan, 2016–2017, animint: interactive grammar of graphics.
- Anuj Khare, 2016, iregnet: regularized interval regression.
- Qin Wenfeng, 2016, re2r: regular expressions.
- Akash Tandon, 2016, Rperform: performance testing R packages.
- Ishmael Belghazi, 2015, bigoptim: stochastic average gradient algorithm.
- Kevin Ferris, 2015, animint: interactive grammar of graphics.
- Tony Tsai, 2015, animint: interactive grammar of graphics.
- Carson Sievert, 2014, animint: interactive grammar of graphics.
- Susan VanderPlas, 2013, animint: interactive grammar of graphics.