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Intro to ML/AI Syllabus

Course Title:

  • Introduction to Machine Learning and Artificial Intelligence

Course Overview:

  • This course is designed for individuals without a software engineering background who are interested in gaining a technical understanding of machine learning and artificial intelligence. The course will cover the basic concepts and techniques used in machine learning and artificial intelligence, including supervised learning, unsupervised learning, and deep learning. In addition, the course will explore the potential applications of machine learning and artificial intelligence in various fields, such as healthcare, finance, and transportation.

Course Goals:

  • Understand the basic concepts and terminology used in machine learning and artificial intelligence.
  • Learn about the different types of machine learning algorithms and how they are applied.
  • Gain an understanding of deep learning and neural networks.
  • Explore the potential applications of machine learning and artificial intelligence in various industries.

Textbook:

  • "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
  • The book is well-written, accessible to beginners, and provides a comprehensive introduction to machine learning concepts, including supervised and unsupervised learning, deep learning, and reinforcement learning. It also covers the practical aspects of working with machine learning algorithms, such as data preparation, model selection, and hyperparameter tuning.
  • In addition, the book includes practical examples and code that readers can follow along with and apply to real-world problems. This makes it an excellent resource for both students who are new to machine learning and professionals who want to expand their knowledge and practical skills.

Course Outline:

Week 1: Introduction to Machine Learning and Artificial Intelligence

  • Introduction to machine learning and artificial intelligence
  • History and evolution of machine learning and artificial intelligence
  • Basic terminology and concepts in machine learning

Week 2: Types of Machine Learning Algorithms

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Examples of machine learning algorithms

Week 3: Deep Learning and Neural Networks

  • Introduction to deep learning
  • Neural networks and their architectures
  • Convolutional neural networks (CNNs)
  • Recurrent neural networks (RNNs)

Week 4: Applications of Machine Learning and Artificial Intelligence

  • Machine learning in healthcare
  • Machine learning in finance
  • Machine learning in transportation
  • Machine learning in social media

Additional Materials:

Readings:

  1. "Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy
  2. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  3. "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
  4. "The Hundred-Page Machine Learning Book" by Andriy Burkov
  5. "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili
  6. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
  7. "Applied Machine Learning" by Kelleher, Tierney, and Becker
  8. "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
  9. "Probabilistic Graphical Models" by Koller and Friedman
  10. "Bayesian Reasoning and Machine Learning" by David Barber

Videos:

  1. "Machine Learning Crash Course" by Google
  2. "Introduction to Neural Networks" by 3Blue1Brown
  3. "The Promise and Peril of Our Machine Learning Future" by Stuart Russell
  4. "Deep Learning Specialization" by Andrew Ng on Coursera
  5. "MIT Introduction to Deep Learning" by Alexander Amini and Ava Soleimany
  6. "Stanford CS229: Machine Learning" by Andrew Ng
  7. "Berkeley CS188: Introduction to Artificial Intelligence" by Pieter Abbeel
  8. "DeepMind X: Deep Learning and Reinforcement Learning Summer School" by DeepMind
  9. "OpenAI's Micro Course on Deep Learning" by OpenAI
  10. "Fast.ai Practical Deep Learning for Coders" by Jeremy Howard