Course Outline
Introduction
Overview of Artificial Intelligence (AI)
- Machine learning
- Computational intelligence
Understanding the Concepts of Neural Networks
- Generative networks
- Deep neural networks
- Convolution neural networks
Understanding Various Learning Methods
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Semi-supervised learning
Other Computational Intelligence Algorithms
- Fuzzy systems
- Evolutionary algorithms
Exploring Artificial Intelligence Approaches to Optimization
- Choosing AI Approaches Effectively
Learning about Stochastic Dynamic Programming
- Relationship with AI
Implementing Mechatronic Applications with AI
- Medicine
- Rescue
- Defense
- Industry-agnostic trend
Case Study: The Intelligent Robotic Car
Programming the Major Systems of a Robot
- Planning the Project
Implementing AI Capabilities
- Searching and Motion Control
- Localization and Mapping
- Tracking and Controlling
Summary and Next Steps
Requirements
- Basic understanding of computer science and engineering
Audience
- Engineers
Testimonials (3)
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
Course - Introduction to the use of neural networks
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.