Introduction to Artificial Intelligence

Objective

• The study of fundamentals and practical issues of artificial intelligence and machine learning.

References

• John Paul Mueller and Luca Massaron, Artificial Intelligence for Dummies, 2nd Ed., John Wiley & Sons, 2022.
• Ashwin Pajankar and Aditya Joshi, Hands-on Machine Learning with Python - Implement Neural Network Solutions with Scikit-learn and PyTorch, Apress, 2022.
• Ameet V. Joshi, Machine Learning and Artificial Intelligence, 2nd Ed., Springer, 2023.
• Pablo Rivas, Deep Learning for Beginners - A beginner's guide to getting up and running with deep learning from scratch using Python, Packt Publishing, 2020.

Lecture Note

Outline

Introduction to Artificial Intelligence

• John Paul Mueller and Luca Massaron, Artificial Intelligence for Dummies, 2nd ed., John Wiley & Sons, 2022.
• Part I Introducing AI
  • Chapter 1 Introducing AI
  • Chapter 2 Defining the Role of Data
  • Chapter 3 Considering the Use of Algorithms
  • Chapter 4 Pioneering Specialized Hardware
• Part II Considering the Uses of AI in Society
• Part III Working with Software-Based AI Applications
• Part IV Working with AI in Hardware Applications
• Part V Considering the Future of AI
• Part VI The Part of Tens

Machine Learning and Deep Learning

• A Taxonomy of Machine Learning/ Deep Learning (from the Internet)
• Ashwin Pajankar and Aditya Joshi, Hands-on Machine Learning with Python - Implement Neural Network Solutions with Scikit-learn and PyTorch, Apress, 2022.
• Section 1 Python for Machine Learning
  • Chapter 1 Getting Started with Python 3 and Jupyter Notebook
  • Chapter 2 Getting Started with NumPy
  • Chapter 3 Introduction to Data Visualization
  • Chapter 4 Introduction to Pandas
• Section 2 Machine Learning Approaches
  • Chapter 5 Introduction to Machine Learning with Scikit-learn
  • Chapter 6 Preparing Data for Machine Learning
  • Chapter 7 Supervised Learning Methods: Part 1
  • Chapter 8 Tuning Supervised Learners
  • Chapter 9 Supervised Learning Methods: Part 2
  • Chapter 10 Ensemble Learning Methods
  • Chapter 11 Unsupervised Learning Methods
• Section 3 Neural Networks and Deep Learning

Grading

• Assignments
  • Submission List
• Midterm Presentation
  
• Final Presentation
  
• Participation
• Semester Grading
  • Semester Grading

Prerequisites

• Intention and attendance