About the Program

Machine learning is the science of getting computers to act without being explicitly programmed. ML is also a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Because of new computing technologies, machine learning today was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new, but one that has gained fresh momentum.

Objectives & outcome

At the end of this programme participants will be able to:

• Define AI & Machine Learning
• Describe the effectiveness of AI and machine learning
• Understand a wide variety of learning algorithms.
• Illustrate the AI industry Deep Dive
• Subfields of machine learning
• Cross-disciplinary fields involving machine learning
• Explain the machine learning frameworks
• Identify the types of machine learning algorithms
• Identify the machine learning methods
• Applications of machine learning

Who must attend?

• Marketer, Researcher, Finance, Supply chain, and Sales, Operation and Business executives and organizational talent-pool participants who desire actionable tools and skills to reinforce their work and process.
• Seasoned managers who want to brush up on new skills related to automation and process
• Senior managers who want practical tools and skills in leading staff members towards enhanced problem solving and decision-making through data analysis.

Program outline

This course provides a broad introduction to machine learning, data-mining, and statistical pattern recognition. Topics include:

• Supervised learning (generative/discriminative learning, parametric/nonparametric learning, neural networks, and support vector machines);
• Unsupervised learning (clustering, dimensionality reduction, kernel methods);
• Learning theory (bias/variance tradeoffs; VC theory; large margins); and
• Reinforcement learning and adaptive control.

Training Methodology

Peer learning, Focus group Discussion, Interactive presentation, Reflection, Experience learning and Evaluation practice.