Artificial Intelligence

Artificial Intelligence Foundations: Principles is the first part of this course designed as an introduction to artificial intelligence (AI), covering foundational concepts, data handling, and common misconceptions. Learners will explore how AI works through hands-on, no-code experiences in machine learning, generative AI, and automation tools. Real-world applications are highlighted across industries, with case studies and emerging trends shaping the future of AI. Emphasis is also placed on ethical AI practices, including fairness, privacy, and regulatory considerations for responsible use.

Artificial Intelligence Foundations: Practice is the second part of this course designed as an introduction to artificial intelligence (AI), covering foundational concepts, data handling, and common misconceptions. Learners will explore how AI works through hands-on, no-code experiences in machine learning, generative AI, and automation tools. Real-world applications are highlighted across industries, with case studies and emerging trends shaping the future of AI. Emphasis is also placed on ethical AI practices, including fairness, privacy, and regulatory considerations for responsible use. In this course, students will apply concepts learned in AIN 1000.

Artificial Intelligence Foundations: Principles and Practice is a course designed as an introduction to artificial intelligence (AI), covering foundational concepts, data handling, and common misconceptions. Learners will explore how AI works through hands-on, no-code experiences in machine learning, generative AI, and automation tools. Real-world applications are highlighted across industries, with case studies and emerging trends shaping the future of AI. Emphasis is also placed on ethical AI practices, including fairness, privacy, and regulatory considerations for responsible use.

Applied Artificial Intelligence: Principles is the first part of a course designed to introduce the core ideas of AI and its use in solving real-world problems. Learners will understand how AI systems interact with data and make decisions to solve problems. The course builds on learners’ curiosity and desire without requiring prior experience with AI. By the end of the course, the learners will be able to understand the basics of AI and develop simple AI-powered applications.

Applied Artificial Intelligence: Practice is the second part of a course designed to introduce the core ideas of AI and its use in solving real-world problems. Learners will understand how AI systems interact with data and make decisions to solve problems. The course builds on learners’ curiosity and desire without requiring prior experience with AI. By the end of the course, the learners will be able to understand the basics of AI and develop simple AI-powered applications. In this course, students will apply concepts learned in AIN 1100.

Applied Artificial Intelligence: Principles and Practice is a course designed to introduce the core ideas of AI and its use in solving real-world problems. Learners will understand how AI systems interact with data and make decisions to solve problems. The course builds on learners’ curiosity and desire without requiring prior experience with AI. By the end of the course, the learners will be able to understand the basics of AI and develop simple AI-powered applications.

Deep Learning and Its Applications: Principles is the first part of a course designed for students to explore how AI driven systems understand image and textual information, and recognize patterns from complex data for image and language tasks. Through hands-on approaches, learners will develop and train AI-driven systems for applications such as image recognition, object detection, and voice assistants. Furthermore, learners will understand the evaluation techniques to decipher the behavior of the AI-based systems and apply optimization techniques for performance enhancement of the AI-based systems.

Deep Learning and Its Applications: Practice is the second part of a course designed for students to explore how AI driven systems understand image and textual information, and recognize patterns from complex data for image and language tasks. Through hands-on approaches, learners will develop and train AI-driven systems for applications such as image recognition, object detection, and voice assistants. Furthermore, learners will understand the evaluation techniques to decipher the behavior of the AI-based systems and apply optimization techniques for performance enhancement of the AI-based systems. In this course, students will apply concepts learned in AIN 1200.

Deep Learning and Its Applications: Principles and Practice is a course designed for students to explore how AI driven systems understand image and textual information, and recognize patterns from complex data for image and language tasks. Through hands-on approaches, learners will develop and train AI-driven systems for applications such as image recognition, object detection, and voice assistants. Furthermore, learners will understand the evaluation techniques to decipher the behavior of the AI-based systems and apply optimization techniques for performance enhancement of the AI-based systems.

Large Language Models (LLM): Principles is the first part of a course which focuses on the technology behind generative language models (e.g. ChatGPT). Learners will understand how LLMs comprehend and generate human readable language, and apply the knowledge to develop intelligent language-based applications such as chatbots, and writing assistants. Furthermore, learners will be exposed to the challenges of using LLMs in practice and gain an understanding of the ethical implications of LLMs in real-world applications.

Large Language Models (LLM): Practice is the second part of a course which focuses on the technology behind generative language models (e.g. ChatGPT). Learners will understand how LLMs comprehend and generate human readable language, and apply the knowledge to develop intelligent language-based applications such as chatbots, and writing assistants. Furthermore, learners will be exposed to the challenges of using LLMs in practice and gain an understanding of the ethical implications of LLMs in real-world applications. In this course, students will apply concepts learned in AIN 1300.

Large Language Models (LLM): Principles and Practice is a course which focuses on the technology behind generative language models (e.g. ChatGPT). Learners will understand how LLMs comprehend and generate human readable language, and apply the knowledge to develop intelligent language-based applications such as chatbots, and writing assistants. Furthermore, learners will be exposed to the challenges of using LLMs in practice and gain an understanding of the ethical implications of LLMs in real-world applications.