Search⌘ K
AI Features
Home
Courses
Optimization for Machine Learning with NumPy and SciPy

Challenge: Convex Optimization

Understand how to frame parameter estimation as a convex optimization problem by using maximum likelihood estimation on beta-distributed data. Learn to implement the mle_estimate function that finds the shape parameter of a beta distribution from viewer ratings.

We'll cover the following...

Problem statement

Suppose a new movie is released and you want to estimate the probability that a random viewer will like it. You assume that this probability follows a beta distribution with a fixed lower bound of 00 and an upper bound of 11. You also assume that the shape parameter of the beta distribution θ\theta is unknown, and you want to find its maximum likelihood estimate.

Mathematical formulation

You collect some data by asking nn ...