Artificial Intelligence Foundations Course

Description

Welcome to Artificial Intelligence Foundations Course. In this course, you'll learn:

1. Introduction to Artificial Intelligence:

   • Explore the history, evolution, and various branches of AI.
   • Understand the ethical implications and societal impact of AI.
   • Gain insights into the current state and future trends of AI technology.

2. Mathematics for AI:

   • Develop a strong mathematical foundation essential for AI applications.
   • Cover concepts such as linear algebra, calculus, and probability theory.
   • Learn how to apply mathematical principles to solve AI problems.

3. Knowledge Representation in AI - Part 1:

   • Examine different methods for representing knowledge in AI systems.
   • Explore symbolic representation and logical reasoning techniques.

4. Knowledge Representation in AI - Part 2:

   • Dive deeper into knowledge representation techniques.
   • Explore ontologies, semantic networks, and other advanced topics.

5. Machine Learning - Part 1:

   • Introduce the basics of machine learning algorithms.
   • Cover supervised and unsupervised learning, and regression.

6. Machine Learning - Part 2:

   • Explore advanced machine learning concepts.
   • Discuss ensemble methods, dimensionality reduction, and model evaluation.

7. Deep Learning:

   • Understand the fundamentals of neural networks.
   • Explore deep learning architectures, including convolutional and recurrent neural networks.

8. Natural Language Processing:

   • Study the processing and understanding of human language by machines.
   • Cover topics such as text analysis, sentiment analysis, and language generation.

9. Computer Vision:

   • Explore the principles and applications of computer vision.
   • Discuss image recognition, object detection, and image generation.

10. Robotics:

    • Introduce the integration of AI in robotics.
    • Explore topics such as robot perception, motion planning, and control.

11. Building AI Applications:

    • Learn the practical aspects of developing AI applications.
    • Discuss real-world case studies and hands-on projects.
    • Understand the challenges and considerations in deploying AI solutions.