We are living in a new age of widespread remote, online learning. Whether it's homeschool parents turning to online resources to help plan lessons, new families looking for activities for their housebound kids over the summer, or high schoolers looking for additional test prep help, the internet is becoming a virtual classroom for a growing number of kids. And the good news is, the quality of online learning platforms has only grown to meet this demand. Some offer games that teach young children in a fun, engaging way that barely feels like school, while others offer in-depth curriculums in foreign languages for students whose parents only speak one language. So what should you look for when searching for a good online learning platform?
A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. In this course, we'll explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables.
This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: value-iteration, policy-iteration and Q-learning. Q-learning is a model free approach so it is state-of-the-art approach. It learns the optimal policy by interacting with the environment.
"In the process of learning to code, people learn many other things. They are not just learning to code, they are coding to learn," Mitchel Resnick, professor at the Massachusetts Institute of Technology (MIT) Media Lab, wrote in an EdSurge article. "In addition to learning mathematical and computational ideas (such as variables and conditionals), they are also learning strategies for solving problems, designing projects, and communicating ideas." Resnick adds that these skills are useful to everyone "regardless of age, background, interests, or occupation."
By exploiting the duality between boosting and online learning, we present a boosting framework which proves to be extremely powerful thanks to employing the vast knowledge available in the online learning area. Using this framework, we develop various algorithms to address multiple practically and theoretically interesting questions including sparse boosting, smooth-distribution boosting, agnostic learning and, as a by-product, some generalization to double-projection online learning algorithms. Papers published at the Neural Information Processing Systems Conference.