may be hard/impossible:modern optimization techriques help 4.Find the parameter values such that the derivatives are zero 3.Write down the derivative of the g likelihood w..t.each parameter may require summing over hidden variables,i.e..inference 2.Write down the likelihood of the data as a function of the parameters requires substantial insight and sometimes new models 1.Choose a parameterized family of modes to describe the data Maximum likelihood assumes uniform prior,OKor large data sets MAP kearning balances complxity with accuracy on training data Full Bayesian learning gives best possible predictions but is intractableSummary Full Bayesian learning gives best possible predictions but is intractable MAP learning balances complexity with accuracy on training data Maximum likelihood assumes uniform prior, OK for large data sets 1. Choose a parameterized family of models to describe the data requires substantial insight and sometimes new models 2. Write down the likelihood of the data as a function of the parameters may require summing over hidden variables, i.e., inference 3. Write down the derivative of the log likelihood w.r.t. each parameter 4. Find the parameter values such that the derivatives are zero may be hard/impossible; modern optimization techniques help Chapter 20, Sections 1–3 13