Machine/Statistical Learning
This course will survey modern methods
for ``directed learning'' where we have
a specific target variable we are trying
do predict and ``undirected learning'' where
we seek a simplifying structure for a system.
Our emphasis will be on computing examples in R
(and hopefully some Python, and maybe some C++).
Course Materials will be available at
http://www.rob-mcculloch.org/.
Grades will be based on weekly assignments (50%)
and a final project (50%).
Both of these may be done in groups.
There is no required text but useful books are:
``Computer Age Statistical Inference''
by Efron and Hastie.
``Practical Machine Learning with H20''
by Darren Cook.
``Machine Learning with R''
by Brett Lantz
``An Introduction to Statistical Learning''
by James, Witten, Hastie, Tibshirani
``Machine Learning''
by Kevin Murphy
``The Elements of Statistical Learning''
by Hastie, Tibshirani, Friedman
``Bayesian Reasoning and Machine Learning''
by Barber
Topics:
Directed:
Naive Bayes
Trees
Ensemble modeling: Random Forests and Boosting
Neural Nets
Support Vector Machines
Gaussian Processes
Bayesian Networks
State Space Models and Hidden Markov Models
Undirected:
Clustering
Mixture Modeling
Dimension Reduction
Latent Dirichlet Allocation for Text data