Bayesian learning

In the maximum-likelihood approach to learning, we try to find the most optimal parameters for our model that maximizes our likelihood function. But data in real life is usually really noisy, and in most cases, it doesn't represent the true underlying distribution. In such cases, the maximum-likelihood approach fails. For example, consider tossing a fair coin a few times. It is possible that all of our tosses result in either heads or tails. If we use a maximum-likelihood approach on this data, it will assign a probability of 1 to either heads or tails, which would suggest that we would never get the other side of the coin. Or, let's take a less extreme case: let's say we toss a coin 10 times and get three heads and seven tails. In this case, a maximum-likelihood approach will assign a probability of 0.3 to heads and 0.7 to tails, which is not...