Artificial Intelligence Foundations Course

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.

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.

Knowledge Representation in AI - Part 1:Examine different methods for representing knowledge in AI systems.Explore symbolic representation and logical reasoning techniques.

Knowledge Representation in AI - Part 2:Dive deeper into knowledge representation techniques.Explore ontologies, semantic networks, and other advanced topics.

Machine Learning - Part 1:Introduce the basics of machine learning algorithms.Cover supervised and unsupervised learning, and regression.

Machine Learning - Part 2:Explore advanced machine learning concepts.Discuss ensemble methods, dimensionality reduction, and model evaluation.

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

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

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

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

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.