Description

Deep Learning techniques belong to the machine learning paradigm. In this course, students will gain insight into the Deep learning algorithms, how they work, their strengths, challenges that lie ahead of them and when to apply them. We will investigate various Deep Learning models from the supervised and unsupervised learning domain. We will examine various existing pipelines used for handling real-world applications. The focus will be on Artificial Neural Networks (ANN), Convolution Neural Networks (CNN), and various generative models.

Program

Bachelor of Computer Science - 144 CRs

Objectives

• 1. Explain the basic mathematical foundation for machine learning techniques
  2. Identify the main Deep Learning algorithms and their applicability in Machine Learning tasks
  3. Explain details of layered-architectures and their learning algorithms.
  4. Explain how real-world problems are modelled and solved by Deep Learning techniques
  5. Elaborate on the strengths of the state-of-the-art of Deep Learning techniques and the challenges that remain.
  6. Develop programs using a Deep Learning toolbox.
  7. Apply metrics for evaluating outcomes of Deep Learning program and analyzing results.