DSC04

Compulsory

1

On completing the course, the student will be able to:

• Develop an appreciation for what is involved in learning from data.
• Explain a wide variety of learning algorithms.
• Understand how to apply a variety of learning algorithms to datasets.
• Know how to perform evaluation of learning algorithms and model selection.

• Search for, analysis and synthesis of data and information, with the use of the necessary technology
• Decision Making
• Teamwork
• Production of free, creative, and inductive thinking

The course introduces fundamental concepts and tools of Machine Learning. The student is exposed to the necessary mathematical/algorithmic background and coding with the Python programming language. The topics covered include:

• Optimization Techniques.
• Linear Regression.
• Linear 2- and multi-class classification.
• Feature Engineering.
• Kernel Methods.
• Fully Connected Neural Networks.
• Tree-Based Learners.