Course Description
This course provides a foundational understanding of Artificial Intelligence (AI), exploring its principles, techniques, and applications. Students will learn about the history and evolution of AI, key concepts such as machine learning, neural networks, natural language processing, and computer vision, and the ethical implications of AI technologies. The course covers problem-solving methods, search algorithms, knowledge representation, and reasoning. Through hands-on projects and case studies, students will gain practical experience in implementing AI solutions.
Course Objectives
By the end of this course, students will be able to:
- Understand Core Concepts: Define and explain fundamental AI concepts, including machine learning, neural networks, natural language processing, and robotics.
- Apply Problem-Solving Techniques: Utilize search algorithms, optimization methods, and heuristic techniques to solve AI problems.
- Implement AI Models: Develop basic AI models and algorithms using programming tools and frameworks such as Python, TensorFlow, or PyTorch.
- Explore Real-World Applications: Analyze case studies and real-world applications of AI in fields like healthcare, finance, autonomous systems, and more.
Course Structure
Module 1: Foundations of AI
Lessons
- Core concepts of Artificial Intelligence
- The historical Journey of Artificial Intelligence
- Types and Categorization of Artificial Intelligence
Trainer
Sunganani Stasha
