An in-depth study of machine learning, to impart an
understanding of the major topics in this area, the
capabilities and limitations of existing methods, and
research topics in this field.
We cover Inductive learning, including decision-tree and
neural-network approaches, Bayesian methods, computational
learning theory, instance-based learning, explanation-based
learning, reinforcement learning, nearest neighbor methods,
PAC-learning, inductive logic programming, genetic
algorithms, unsupervised learning, linear and nonlinear
dimensionality reduction, kernels methods, graphical
models, and regression modeling.