The spark of intelligence — a single neuron ignites a journey that traverses cultures, empires, and centuries.

Countless philosophers, monks, religions, biologists, and psychologists have tried to understand the nature of consciousness and intelligence. But in the words of Lord Kelvin, “You do not really understand something unless you can explain it in mathematics”. Hypatia, visionary head of the ancient Library of Alexandria, discerns this profound void in humanity’s understanding. In the events that follow, she dispatches Alexandria’s finest minds — the knowledge scavengers — on a global mission to uncover the equation of intelligence.

With the Library’s announcement of a handsome reward for uncovering the equation of intelligence, a knowledge scavenger sets out to Baghdad’s House of Wisdom. Determined to establish a rigorous framework for testing any algorithm that purports to capture something as abstract as intelligence, he learns many profound lessons. This optional chapter provides foundational insights essential for understanding the algorithms explored in later chapters:

Machine Learning Concepts Covered:

1. Training and Testing
2. Cross Validation
3. Curve Fitting
4. Over-fitting/Underfitting
5. Learning Curves
6. Data Cleaning

“Intelligence cannot be confined.” News reaches the Library of Alexandria about a young man from a humble farm on the outskirts of Rome, once driven by dreams of joining the Roman army. But fate takes a dark turn, and he finds himself enslaved by the very forces he once longed to serve. With each narrow escape from death, fighting for his freedom, he comes to realize that intelligence lies in building accurate mental models and using them to foresee the future. Each event leads him to piece together groundbreaking concepts in linear methods.

Machine Learning Concepts Covered:

1. Weights and Biases
2. Loss Function
3. Gradient descend
4. Linear Regression
5. Sigmoid Function
6. Entropy
7. Logistic Regression
8. Regularization

While the other knowledge scavengers tread the well-worn paths of the East — delving into the intellectual riches of Rome, India, and China — one among them defies convention, veering westward into the uncharted realms. There he stumbles upon a mysterious new world. Deep within the seemingly endless Amazon rainforest, he encounters an ancient tribe who laughs at his belief that humans are the pinnacle of intelligence. They introduce him to other forms of intelligence, more ancient and subtle, and teach him the language of the forest, challenging his perceptions, and inspiring him to take a new approach towards the equation of intelligence.

Machine Learning Concepts Covered:

1. Branching using Entropy and Information Gain
2. Gini Impurity
3. Random Forest

“Intelligence may lie in our ability to adapt to change.” A diary arrives at the Library of Alexandria, written by a monk from the Shaolin temple in China who has endured the brutal deaths of everyone he loved. Torn between the pull of vengeance, and the peaceful teachings of Buddhism, he embarks on a transformative journey, reshaping his beliefs and refining his understanding of reality by continually updating his mental model in response to new evidence. Ultimately, he distills his hard-earned wisdom into the elegant simplicity of Bayes’ theorem.

Machine Learning Concepts Covered:

1. Conditional probability
2. Margins, Likelihood, Posterior
3. The Prior
4. The Bayes Theorem

The Romans, Persians, and Chinese find themselves entangled in the biggest heist the ancient world has ever known, a calculated game of power and intellect. As the intrigue unfolds, each civilization moves with calculated precision, striving to outmaneuver the others by mastering the art of discernment and first principles thinking, using an algorithm called the support vector machines.

Machine Learning Concepts Covered:

1. Support Vectors
2. Maximum Margins
3. Mapping Data to Higher Dimensions
4. The Kernel Trick

In a world fractured by labels and judgment, a Sufi emerges in the ancient city of Konya (modern-day Turkey) ruled my orthodox clerics. He reveals that true intelligence transcends such labels. Preaching unity within diversity, he guides both followers and skeptics to search for the centers of meaning within the soul’s scattered landscapes. Through his teachings, they are compelled to explore the concepts of clustering.

Machine Learning Concepts Covered:

1. K-means Clustering

This Chapter to be held for Season 2 – Going deeper. Stay tuned 🔒

Piece by piece, Hypatia and her elite team of scholars labor to assemble the fragments relayed by knowledge scavengers from across the globe, carefully constructing their first mathematical model of the human brain — a neural network. This daring endeavor sparks fierce criticism from fellow scholars and incites accusations of heresy from the orthodox church.

Machine Learning Concepts Covered:

1. Activation function
2. Neurons and layers
3. Forward and back-propagation
4. Optimization Algorithms
5. Hyperparameters
6. Generalization and Regularization Techniques 

This Chapter to be held for Season 2 – Going deeper. Stay tuned 🔒

Deep in the lush mountains of India, a grand sage breaks his meditation of years to understand mind from the within. A former prince and master at Kalari, an form of ancient Indian martial arts, he leaves everything behind to impart his wisdom in a small temple in China.

Machine Learning Concepts Covered:

1. Word Embeddings
2. Self-Attention
3. Transformers
4. Large Language Models

This Chapter to be held for Season 2 – Going deeper. Stay tuned 🔒

“Nothing is real; all is illusion.” An assassin in Persia masters the art of manipulating minds, learning to mold his identity like clay to exploit the gaps in human perception. With each new guise and deception, he deepens his understanding of the mind’s fragility, mastering how fear, desire, and doubt can deceive even the sharpest of minds, leaving them vulnerable to his carefully crafted illusions.

Machine Learning Concepts Covered:

1. Convolutional Neural Networks
2. Generative Adversarial networks
3. Diffusion Models

This Chapter to be held for Season 2 – Going deeper. Stay tuned 🔒

Faith and science collide as the church and the library stand in fierce opposition, each defending its claim to truth. The bankrupt government struggles to control the swelling riots, while the assassination of prominent figures on both sides fuels an unstoppable wave of unrest, plunging the city into chaos. Amid threats of violence and looming attacks, Hypatia and the scholars at Alexandria race against time to uncover the equation of intelligence.

Machine Learning Concepts Covered:

1. Rewards and Penalties
2. Markov Decision Processes
3. Dynamic Programming for RL
4. Monte Carlo Methods
5. Temporal Difference (TD) Learning
6. Deep Q-Learning (DQN)
7. Policy-Based Methods
8. Actor-Critic Methods

Putting together the final equation of intelligence.