Machine learning techniques enable us to automatically extract features from data to solve predictive tasks, such as speech recognition, object recognition, machine translation, question-answering, anomaly detection, medical diagnosis, prognosis, automatic algorithm configuration, personalization, robot control, time series forecasting and much more.
Learning system adapts to solve new tasks related to previously encountered tasks in a more efficient manner. The course will focus on the introduction of fields of machine learning. Deeper understanding on the core concepts of supervised and unsupervised learning would be enthralled.
In supervised learning we will discuss algorithms that are trained on input data labelled with a desired output.
Example: An image of a face and the name of the person whose face it is and learn a function mapping from the input to the output.
Unsupervised learning aims to discover latent structure in an input signal where no output labels are available, an example of which is grouping web-pages based on the topics they discuss. Faculties will learn the algorithms which underpin many popular machine learning techniques, as well as developing an understanding of the theoretical relationships between these algorithms. The practicals’ will rapt the application of machine learning to a range of real-world problems.