December 2024

1. Ensemble Methods 1.1 Bagging (Bootstrap Aggregating) A method that creates multiple versions of a predictor by training them on random subsets of the training data and aggregating their predictions. Use Cases: Reducing overfitting Improving stability Classification tasks Regression problems Noisy data handling Strengths: Reduces variance Prevents overfitting Parallel processing possible Model stability Handles noisy data Limitations: Increased computation More storage needed Limited bias reduction May lose interpretability Resource intensive Random Forest (as a Bagging...

1. Time Series Analysis 1.1 Classical Methods ARIMA (AutoRegressive Integrated Moving Average) A statistical model that combines autoregression, differencing, and moving average components for time series forecasting. Use Cases: Financial forecasting Sales prediction Weather forecasting Demand planning Traffic prediction Strengths: Handles trends and seasonality Well-understood statistical properties Good for linear relationships Interpretable components Works with stationary data Limitations: Assumes linear relationships Requires stationarity Limited with complex patterns Sensitive to outliers Not suitable for long-term forecasting...

1. Generative Models 1.1 Generative Adversarial Networks (GANs) A framework where two neural networks compete: a generator creating synthetic data and a discriminator trying to distinguish real from fake data. Use Cases: Image synthesis Data augmentation Style transfer Text-to-image generation Video generation Strengths: High-quality synthetic data Learns complex distributions Unsupervised learning Creative applications Continuous improvement through competition Limitations: Training instability Mode collapse Nash equilibrium issues Difficult to evaluate Complex hyperparameter tuning 1.2 Variational Autoencoders (VAEs)...

1. Basic Neural Networks 1.1 Feedforward Neural Networks (FNN) The most basic neural network architecture where information flows in one direction, from input through hidden layers to output, without cycles. Use Cases: Pattern recognition Classification tasks Regression problems Function approximation Feature learning Strengths: Simple to understand Versatile Good for structured data Fast inference Well-studied architecture Limitations: Limited by fixed input size No temporal dependencies May require large training data Prone to overfitting Limited contextual understanding...