MOOC - Machine Learning Engineer
Machine Learning Algorithms
The third unit of this job role provides a comprehensive description of the different Machine Learning algorithms.
Reinforcement Learning Models:
This learning element provides a description of reinforcement learning models focusing on its definition and application domain as well as in its most commonly used algorithms. In addition, this element shows some common terminologies used in the field of reinforcement learning. Furthermore, this element provides insights about policy optimization methods, Q-learning methods and hybrid methods. Finally, the element explains the differences between model-based reinforcement learning and model-free reinforcement learning, as well as, the distinct approaches of model-based reinforcement learning, namely, learn the model and learn given the model.
MLE.U3.E5.PC1
The student is able to define and understand reinforcement learning.
MLE.U3.E5.PC2
The student knows common terminologies used in the field of reinforcement learning.
MLE.U3.E5.PC3
The student knows some of the most commonly used algorithms in reinforcement learning.
MLE.U3.E5.PC4
The student understands model-based reinforcement learning and model-free reinforcement learning as well as their differences.
MLE.U3.E5.PC5
The student understands policy optimization methods, Q-learning methods and hybrid methods as well as list some algorithms for each category.
MLE.U3.E5.PC6
The student understands the different approaches of model-based reinforcement learning: learn the model and learn given the model.
MLE.U3.E5.PC7
The student knows the domain application of reinforcement learning models.