Fundamentals of Artificial Intelligence (5 credits)

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Artificial intelligence (AI) has numerous applications in today’s world. AI has enabled us to build machines that can think rationally and sometimes as a human. AI can be seen everywhere, from self-driving cars to a simple chatbot or even YouTube video recommender. This course is a gentle introduction to AI's basic concepts and methodologies from both theoretical and practical perspectives. The course covers essential intuitions behind different AI methods (e.g., machine learning and deep learning) as well as the business-side topics, like the implications of AI and its effect on today’s industry. By the end of this course, the student will be familiar with modern AI aspects as his/her very first steps on the journey to AI. The student will learn to think outside the box using AI and present AI-based solutions using appropriate methods discussed in the course.

Learning outcomes

  • Learn the fundamental definitions and concepts of AI, Machine Learning, and Deep Learning
  • Get familiar with the history of AI and its primitive applications
  • Understand the concept of data for modern AI
  • Learn the difference between classical AI and modern AI (e.g., Machine Learning)
  • Get familiar with the different types of Machine Learning algorithms and Neural Networks
  • Get familiar with the related fields to AI, such as Computer Vision, Speech Processing, and NLP
  • Understand the influential role of AI in today's industry and get familiar with some industrial applications of AI and Machine Learning
  • Learn the pipeline/workflow of a Machine Learning project
  • The student knows the different job positions related to Data Analysis, AI, and Machine Learning
  • Get familiar with the methods to incorporate AI in a business and the requirements of an AI team
  • Get familiar with some problems of AI, including the concept of bias, the impact of AI on jobs and public privacy, and the adversarial attacks on AI

Table of contents

Chapter 1: Beginning with This Course

  • 1.1. Our Approach in This Course
  • 1.2. About TechClass AI Department
  • 1.3. Your Expectations, Goals, and Knowledge

Chapter 2: Introduction

  • 2.1. What is AI?
  • 2.2. Intelligent Agents
  • 2.3. Thinking Humanly
  • 2.4. Acting Humanly
  • 2.5. Thinking and Acting Rationally
  • 2.6. Chinese Room Argument
  • 2.7. Strong AI vs. Weak AI
  • 2.8. History of AI

Chapter 3: Modern AI

  • 3.1. Role of Data Science in Modern AI
  • 3.2. What is Data?
  • 3.3. Classical AI vs. Machine Learning
  • 3.4. Machine Learning vs. Deep Learning
  • 3.5. Some Applications of AI

Chapter 4: Machine Learning

  • 4.1. General Concepts
  • 4.2. Supervised Learning
  • 4.3. Unsupervised Learning
  • 4.4. Reinforcement Learning
  • 4.5. Neural Networks
  • 4.6. Deep Learning
  • 4.7. Crucial Hints for Model Training
  • 4.8. Machine Learning and Related Fields

Chapter 5: AI and Machine Learning in Industry

  • 5.1. AI as an Indispensable Part of Today’s Industry
  • 5.2. Some Machine Learning Use Cases in Industry
  • 5.3. Pipeline of a Machine Learning Project
  • 5.4. A Closer Look at an AI-based Product
  • 5.5. Different Roles in an AI Team
  • 5.6. Strategies to Incorporate AI in Your Company
  • 5.7. AI Team Requirements in Your Company

Chapter 6: Problems of AI

  • 6.1. Bias in AI
  • 6.2. AI and Privacy
  • 6.3. Adversarial Attacks on AI
  • 6.4. Impact of AI on Jobs

Chapter 7: Our Future with AI

  • 7.1. What Can be Expected from the Future of AI?
  • 7.2. Will Machines Obtain Consciousness?
  • 7.3. Advice for a Better Future with AI

Chapter 8: Final Tasks

  • 8.1. Project
  • 8.2. Self-study Essay
  • 8.3. Congrats! You did it!


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