Reinforcement learning can be understood by using the concepts of agents, environments, states, actions and rewards. This is an area of machine learning; where there’s no answer key, but RL agent still has to decide how to act to perform its task. The agent is inspired by behaviourist psychology who decide how and what actions will taken in an environment to maximize some notion of cumulative reward.
System does self-discovery of patterns, regularities and features etc. from the input data and relations for the input data over output data. Discovering similarities and dissimilarities to forms clusters i.e. self-discovery is main target here. Since the examples given to the learner are unlabeled
Machine learning algorithms “learns” from the observations. When exposed to more observations, the algorithm improves its predictive performance. What’s going to happen to the stock market tomorrow? Is a task of deducing function from labeled training data.
Machine learning is the process of a machine attempting to accomplish a task, independent of human intervention, more efficiently and more effectively with every passing attempt i.e learning phase. At this point, AI- a machine which mimics the human mind, is still a pipe dream. In the middle we have the meat of the pipeline, the model, which is the machine learning algorithm that learns to predict given input data.
Statistics and Machine Learning – Do Statistics and ML walk together like partners?. Is Machine learning just a polished or glorified version of statistics?. There are many questions such as this, at least in my mind. Even today I get these questions from many of my fellow lab members. […]