Machine Learning & Artificial Intelligence (ML &AI)

What is this course about?

Machine learning is the subfield of computer science that, according to Arthur Samuel, gives “computers the ability to learn without being explicitly programmed.” Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term “machine learning” in 1959 while at IBM.

Highlights of workshop

  • Certificate of Participation to all participants.
  • Certificate of Merit to all Zonal Winners.
  • Certificate of Coordination to the student and faculty coordinators.
  • Enhance your Knowledge through various Live Projects.
  • Appreciation letter to faculty coordinators.
Artificial Intelligence
Introduction to Artificial Intelligence
Working with Fuzzy logic Algorithm
Getting started with Fuzzy Logic
Problem Formulation, Defuzzification & Rulebase
Working with Fuzzy Logic ion Simulink
Machine Learning
Introduction to Machine Learning
Applications of Machine Learning
Artificial Intelligence & Machine Learning
Database Mining & Machine Learning
Supervised Learning Introduction & Examples
Unsupervised Learning Introduction & Examples
Linear Regression & implementation
Introduction to Gradient Descent Algorithm
Linear Algebra review
Introduction to Neuron
Introduction to Network Architecture
Designing Neural Network Model
Model Representation Methods
Single Layer Neural Network
Multilayer Neural Network Architecture
Training the Network
Backward Propagation Training
Using the Network
Importing & Exporting Network
Importing & Exporting Training Data
Introduction to Neural Network
Introduction to Dynamic Neural Network
Neural Network Blocks
Working with Genetic Algorithm
Getting started with Genetic Algorithm
Implementing GA
Examples and applications
Case Study: Cancer Detection
Case Study: Character Recognition
Case Study: Iris Clustering
Case Study: Intelligent Washing Machine Design
Case Study: 8 Queens Problem Slover