top of page

Basic Artificial intelligence

Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computersoftware that are capable of intelligent behavior. 
AI is not limited to a physical entity, but could exist an organic system.
 
Major AI researchers and textbooks define this field as "the study and design of intelligent agents", in which an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines". 
 
AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.
 
General intelligence is still among the field's long-term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI.
 
There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.
 
The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, mathematics, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology.
 
The field was founded on the claim that a central property of humans, human intelligence—the sapience of Homo sapiens—"can be so precisely described that a machine can be made to simulate it.
 
Today it has become an essential part of the technology industry, providing the heavy lifting for many of the most challenging problems in computer science.
 
The training offered by TTCOA, will introduce the student to some of the potential career opportunities that currently exist with in the "Artificial Intelligence and Robotics industry.  
 
The programs focuses on the development of Algorithms and the mathematical process that are used to capture and create the data to form paterns used for interpretation and action, by AI.
 
The program offers training that will reinforce the student’s current technology skills, and encourages personal growth and professional development, by advancing their knowledge of advanced Information Technology and their respective management tools. 

Aquatic Robots
Aviation Robots

Curriculum Outline
 

This curriculum provides a solid foundation in AI, covering key concepts and techniques. You can adjust the duration and depth of each topic based on your requirements and the level of the audience. Additionally, incorporating practical exercises, projects, and real-world examples will enhance the learning experience.
 

  1. Introduction to AI

    • Definition of AI and its applications

    • Historical background and key milestones

    • Overview of different AI approaches (symbolic AI, machine learning, deep learning)

  2. Machine Learning Fundamentals

    • Introduction to machine learning concepts

    • Supervised, unsupervised, and reinforcement learning

    • Training, testing, and validation of machine learning models

    • Evaluation metrics and model performance

  3. Deep Learning

    • Introduction to neural networks

    • Building blocks of neural networks (activation functions, layers, loss functions)

    • Training neural networks with back-propagation.

    • Convolutional neural networks for image recognition

    • Recurrent neural networks for sequential data analysis

  4. Natural Language Processing (NLP)

    • Overview of NLP and its applications

    • Text preprocessing (tokenization, stemming, lemmatization)

    • Language modeling and text generation

    • Sentiment analysis and document classification

    • Named Entity Recognition (NER) and Part-of-Speech (POS) tagging

  5. Computer Vision

    • Introduction to computer vision and its applications

    • Image preprocessing (resizing, normalization, augmentation)

    • Object detection and localization

    • Image segmentation and semantic segmentation

    • Generative Adversarial Networks (GANs) for image synthesis

  6. Ethical and Social Implications of AI

    • Bias and fairness in AI algorithms

    • Privacy concerns and data protection

    • AI in the workforce and job displacement

    • Ethical considerations in AI development and deployment

  7. AI Tools and Libraries

    • Introduction to popular AI libraries (e.g., TensorFlow, PyTorch)

    • Hands-on exercises with AI frameworks

    • Working with pre-trained models and transfer learning

    • Deploying AI models in production environments

  8. Future Trends in AI

    • Cutting-edge research and emerging AI technologies

    • Reinforcement learning advancements

    • Explainable AI and interpretability

    • AI and robotics

  • Wix Facebook page
  • Wix Twitter page
  • Wix Google+ page

©2023  Technology Training Centers Of Atlanta L.L.C.

bottom of page