Pay-per-click advertising includes various formats (e.g., search, contextual, and social) with a total investment of more than 140 billion USD per year. An advertising campaign is composed of some subcampaigns—each with a different ad—and a cumulative daily budget. The displacement of the ads is ruled exploiting auction mechanisms. In this seminar, we takl about an algorithm for the online joint bid/budget optimization of pay-per-click multi-channel advertising campaigns. We formulate the optimization problem as a combinatorial bandit problem, in which we use Gaussian Processes to estimate stochastic functions, Bayesian bandit techniques to address the exploration/ exploitation problem, and a dynamic programming technique to solve a variation of the Multiple-Choice Knapsack problem. We present experimental results showing that our algorithm is able to provide good performance both in simulation (using a synthetic setting generated from real data from Yahoo!) and in a real-world application over an advertising period of two months.