Course Catalogue / AI - Artificial Intelligence

This AI course introduces students to the fundamentals of artificial intelligence, including concepts such as search algorithms, robotics, machine learning, natural language processing, and computer vision. Students will gain an understanding of the algorithms and techniques used to create intelligent systems as well as the ethical and legal considerations associated with their use. Through hands-on projects and lectures, students will gain the skills necessary to build and deploy AI-based applications.

AIU HS

AI course is a course of study focused on the fundamentals of artificial intelligence, including its principles, algorithms, and applications. It covers topics such as robotics, machine learning, natural language processing, computer vision, and more. The goal of the course is to provide students with a foundational understanding of the field and the tools to apply it in their own projects.

Develop an understanding of the fundamental concepts, principles and techniques of Artificial Intelligence (AI).
Learn how to apply AI algorithms to solve problems in a variety of domains.
Understand the different AI architectures and approaches such as supervised, unsupervised and reinforcement learning.
Analyze the potential of AI in a variety of fields and applications.
Design and implement AI systems that can interact with the environment.
Develop and test AI models for a variety of problems.
Understand the ethical and social implications of AI.
Evaluate the performance of AI systems.

AI - Artificial Intelligence
- ChatGPT
  - Explanation
  - How to use ChatGPT
- Dall-E
  - Explanation
  - How to use Dall-E
- Midjourney
  - Explanation
  - How to use Midjourney
- Lesson 1: Introduction of AI and its importance
  - Introduction to AI
  - Overview of AI technologies
  - Applications of AI in various fields
  - Ethical considerations and future implications of AI
- Lesson 2: Machine Learning
  - What is Machine Learning?
  - Basic algorithms and model training
  - Applications and case studies in ML
- Lesson 3: Deep Learning and Neural Networks
  - Introduction to neural networks and deep learning
  - Architecture of neural networks
  - Deep learning applications like image and speech recognition
- Lesson 4: Natural Language Processing (NLP)
  - Basics of natural language processing.
  - Applications of NLP like chatbots, translators, and voice assistants
  - Challenges and future of NLP
- Lesson 5: Computer Vision
  - Introduction to computer vision
  - Image recognition, object detection, and facial recognition
- Lesson 6: Future AI Robots
  - Introduction to robotics.
  - Application of AI in robotic control and autonomy
  - Real-world examples of AI robots
  - Future AI Robots
- Lesson 7: Ethical and Social Implications of AI
  - Bias and fairness in AI
  - Legal and privacy issues
  - Social implications and job displacement

Introduction: Introduce the course and its objectives. Explain the basics of AI and how it is used in various industries.
Foundations: Cover the basic mathematical and computing concepts necessary to understand AI. Topics may include probability, linear algebra, calculus, optimization, and computer science.
Algorithms & Techniques: Introduce the most important algorithms and techniques used in AI. Topics may include supervised and unsupervised learning, deep learning, reinforcement learning, and natural language processing.
Applications: Show students how to apply AI to real-world problems. Topics may include computer vision, robotics, and autonomous agents.
Advanced Topics: Explore more complex topics in AI. Topics may include generative models, meta-learning, and transfer learning.
Practical Assignments: Give students hands-on experience with AI through programming assignments and projects.
Final Project: Have students complete a final project to demonstrate their understanding of the course material.

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