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Day-to-Day Breakdown

Week 1:

Day 1: Introduction to Machine Learning and Artificial Intelligence

  • Introduction to the course and its goals
  • Brief history of machine learning and artificial intelligence
  • Basic terminology and concepts in machine learning Readings: "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig (Chapter 1) Videos: "Machine Learning Crash Course" by Google

Day 2: Types of Machine Learning Algorithms

  • Supervised learning and examples
  • Unsupervised learning and examples Readings: "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili (Chapter 2) Videos: "Introduction to Neural Networks" by 3Blue1Brown

Day 3: Reinforcement Learning

  • Definition and examples of reinforcement learning
  • Markov Decision Processes (MDPs) Readings: "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto (Chapter 1) Videos: "Berkeley CS188: Introduction to Artificial Intelligence" by Pieter Abbeel (lecture on reinforcement learning)

Day 4: Examples of Machine Learning in Practice

  • Machine learning in healthcare
  • Machine learning in finance Readings: "Applied Machine Learning" by Kelleher, Tierney, and Becker (Chapter 1 and 2) Videos: "The Promise and Peril of Our Machine Learning Future" by Stuart Russell

Day 5: Applications of Machine Learning in Social Media

  • Sentiment analysis and opinion mining
  • Personalized recommendations Readings: "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili (Chapter 8) Videos: "DeepMind X: Deep Learning and Reinforcement Learning Summer School" by DeepMind

Week 2:

Day 1: Linear Regression

  • Introduction to linear regression
  • Least squares and maximum likelihood Readings: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron (Chapter 4) Videos: "MIT Introduction to Deep Learning" by Alexander Amini and Ava Soleimany (lecture on linear regression)

Day 2: Logistic Regression

  • Introduction to logistic regression
  • Maximum likelihood estimation Readings: "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili (Chapter 3) Videos: "MIT Introduction to Deep Learning" by Alexander Amini and Ava Soleimany (lecture on logistic regression)

Day 3: Decision Trees

  • Introduction to decision trees
  • CART and ID3 algorithms Readings: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron (Chapter 6) Videos: "Stanford CS229: Machine Learning" by Andrew Ng (lecture on decision trees)

Day 4: Random Forests

  • Introduction to random forests
  • Advantages and disadvantages Readings: "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili (Chapter 4) Videos: "OpenAI's Micro Course on Deep Learning" by OpenAI (lecture on random forests)

Day 5: Support Vector Machines (SVMs)

  • Introduction to SVMs
  • Linear and nonlinear SVMs Readings: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron (Chapter 5) Videos: "Stanford CS229: Machine Learning" by Andrew Ng (lecture on SVMs)

Week 3:

Day 1: Introduction to Deep Learning

  • What is deep learning?
  • Neural networks and layers Readings: "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Chapter 1) Videos: "MIT Introduction to Deep Learning" by Alexander Amini and Ava Soleimany (lecture on deep learning)

Day 2: Convolutional Neural Networks (CNNs)

  • Introduction to CNNs
  • Architecture of CNNs Readings: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron (Chapter 14) Videos: "Convolutional Neural Networks" by 3Blue1Brown

Day 3: Recurrent Neural Networks (RNNs)

  • Introduction to RNNs
  • LSTM and GRU cells Readings: "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Chapter 10) Videos: "Recurrent Neural Networks" by 3Blue1Brown

Day 4: Autoencoders

  • Introduction to autoencoders
  • Denoising autoencoders and variational autoencoders Readings: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron (Chapter 15) Videos: "Autoencoders and Generative Models" by Stanford CS231n

Day 5: Generative Adversarial Networks (GANs)

  • Introduction to GANs
  • Architecture of GANs Readings: "Generative Deep Learning" by David Foster (Chapter 1) Videos: "Generative Adversarial Networks" by 3Blue1Brown

Week 4:

Day 1: Introduction to Natural Language Processing (NLP)

  • What is NLP?
  • Applications of NLP Readings: "Speech and Language Processing" by Jurafsky and Martin (Chapter 1) Videos: "MIT Introduction to Deep Learning" by Alexander Amini and Ava Soleimany (lecture on NLP)

Day 2: Word Embeddings

  • Introduction to word embeddings
  • Word2Vec and GloVe algorithms Readings: "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper (Chapter 6) Videos: "Efficient Estimation of Word Representations in Vector Space" by Tomas Mikolov

Day 3: Sequence-to-Sequence Models

  • Introduction to sequence-to-sequence models
  • Encoder-decoder architecture Readings: "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Chapter 10) Videos: "Sequence-to-Sequence Models" by Stanford CS224n

Day 4: Transfer Learning

  • Introduction to transfer learning
  • Fine-tuning and feature extraction Readings: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron (Chapter 14) Videos: "Transfer Learning" by Stanford CS231n

Day 5: Ethical Considerations in AI

  • Bias and fairness in machine learning
  • Responsibility and accountability in AI development Readings: "Artificial Intelligence and Ethics" by Wendell Wallach and Colin Allen (Chapter 1 and 2) Videos: "TED Talk: How to Keep Human Bias Out of AI" by Kriti Sharma

Additional Materials:

  • Readings:
    1. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
    2. "Python Machine Learning" by Sebastian Rasch