Day-to-Day Breakdown

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

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)

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

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