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Course Introduction

Artificial Intelligence Course Basics

Course Text

The course provides a comprehensive exploration of Artificial Intelligence (AI) and its various applications across different domains. We begin by laying the groundwork with an overview of AI, delving into its fundamental concepts and potential applications. Building upon this foundation, we delve into Machine Learning (ML), exploring its principles and basic concepts, before delving deeper into advanced topics and techniques.
A significant portion of the course is dedicated to Deep Learning (DL), where we explore its fundamental principles and distinctions from traditional Machine Learning approaches. Additionally, we discuss strategies for acquiring skills in AI, providing insights into available resources and educational pathways to foster proficiency in this rapidly evolving field.
Practical implementation is a key focus, with discussions on coding in AI and available APIs for AI programming. We explore how AI is applied in the Information Technology (IT) field, demonstrating the creation of HTML websites using ChatGPT prompt engineering, and discussing techniques for detecting eye and mouth movements using AI.
In later sections, we shift our focus to specific industries, such as the automotive sector, where AI plays a crucial role in areas like autonomous driving and vehicle intelligence. We also explore the application of AI in Requirements Management, discussing how AI technologies streamline the requirements engineering process and enhance efficiency.
Throughout the course, learners gain a comprehensive understanding of AI, Machine Learning, and Deep Learning, along with practical skills and insights into their real-world applications. By the end of the course, students will be equipped with the knowledge and tools necessary to navigate the rapidly evolving landscape of Artificial Intelligence.
This course will be updated regularly so stay tuned

Course Quiz

Course: Artificial Intelligence Course Basics

1. What does AI stand for?

2. Which of the following is NOT a subfield of artificial intelligence?

3. What type of learning algorithm allows AI systems to improve their performance over time without being explicitly programmed?

4. Which AI technique is inspired by the structure and function of the human brain?

5. Which programming language is commonly used for implementing AI algorithms and applications?

6. What does the Turing Test assess?

7. What is the primary goal of natural language processing (NLP)?

8. What is the term for the process of teaching a machine learning model with labeled data?

9. Which AI application involves the use of algorithms to identify patterns and anomalies in large datasets?

10. Which company's AI system, AlphaGo, famously defeated human champions in the game of Go?

11. What is generative AI primarily used for?

12. Which of the following is a popular model architecture used in generative AI for creating realistic images?

13. What are the two main components of a Generative Adversarial Network (GAN)?

14. Which generative AI model is known for its use in text generation, such as writing essays or articles?

15. What is the primary objective of the generator in a GAN?

16. Which technique is often used by generative AI models to create diverse and high-quality text responses?

17. What is a common application of generative AI in the field of healthcare?

18. Which of the following is an example of a variational autoencoder (VAE)?

19. What role does the discriminator play in a GAN?

20. Which model, developed by OpenAI, is known for generating human-like text and has been widely used for conversational AI?