Specialization: Machine Learning

Machine learning is an area of computer science in which algorithms learn and improve from experience. But systems are not explicitly programmed to do so. It comprises of various sub-fields like supervised learning, unsupervised learning, semi-supervised and reinforcement learning. From web search to bioinformatics machine learning has wide applications. In reinforcement learning there is interaction between an agent and environment and learning is through rewards obtained. Typical applications are in robotics, autonomous vehicles and control in networks. The important factor in designing reinforcement learning algorithms is the balance between exploration and exploitation. Performance of algorithms can be studied empirically in applications or theoretically using sample complexity and regret bounds. The main challenge with these algorithms is how to deal with large state and action spaces, which is the case in real life applications.

Sub Areas under Machine Learning:

  • Supervised Learning
  • Unsupervised Learning
  • Deep Learning
  • Reinforcement Learning
  • Robotics