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Computer Science

Machine Learning

Activation Functions in Neural Networks

Bias-Variance Tradeoff

Computer Vision Fundamental Concepts

Cross-validation Techniques

Data Preprocessing for Machine Learning

Deep Learning Fundamentals

Data Imbalance Handling

Dimensionality Reduction Methods

Ensemble Learning Techniques

Evaluation Metrics for Classification

Feature Engineering Techniques

Generative Models

Gradient Descent Variants

Hyperparameter Tuning

Machine Learning Best Practices

Loss Functions Explained

Machine Learning Security and Privacy

Machine Learning Pipelines

Machine Learning Frameworks

Model Explainability and Interpretability

Multiclass Classification Strategies

Natural Language Processing Terminology

Model Selection Criteria

Neural Network Architectures

Performance Optimization in Machine Learning

Time Series Analysis Techniques

Reinforcement Learning Concepts

Statistical Tests for Model Comparison

Time Series Analysis Techniques

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Autoregressive Integrated Moving Average (ARIMA)

ARIMA models are used for analyzing and forecasting time series data by integrating autoregressive (AR) and moving average (MA) models. It is suitable for non-stationary data and helps in identifying trends and other seasonal factors in the series. Problems solving: time series forecasting, financial forecasting, weather forecasting.

Exponential Smoothing

Exponential Smoothing is a rule of thumb technique for smoothing time series data using the exponential window function. Problems solving: it is commonly used for smoothing data, short term forecasting, and eliminating random variation.

Dynamic Time Warping (DTW)

DTW is an algorithm for measuring similarity between two temporal sequences which may vary in time or speed. Problems solving: useful for comparing sequences with different lengths and speeds, such as audio, video, or other temporal sequences.

Wavelet Transform

Wavelet Transform is a mathematical technique for hierarchical decomposition of time series. Problems solving: it's used for signal processing, image compression, and denoising time series data.

Kalman Filter

The Kalman Filter is an algorithm that uses a series of measurements observed over time to estimate the state of a dynamic system. Problems solving: helps in trajectory estimation, navigation systems, and econometrics.

Seasonal Decomposition of Time Series (STL)

STL is a flexible technique for decomposing a time series into seasonal, trend, and residual components. Problems solving: analyzing seasonal patterns, detrending data, noise reduction.

Hidden Markov Models (HMM)

HMMs are stochastic models used to represent systems with unobservable (hidden) states. Problems solving: they are great for sequence classification, speech recognition, and biological data analysis.

Time Series Clustering

Time Series Clustering involves grouping similar time series together based on certain distance measures. Problems solving: useful for discovering similar patterns among data, market segmentation, and anomaly detection.

Cointegration

Cointegration is a statistical property of a collection of time series variables which indicates that a linear combination of them has a lower order of integration. Problems solving: used in econometrics to test for a long-run equilibrium relationship between time series.

Vector Autoregression (VAR)

VAR is a statistical model used to capture the linear interdependencies among multiple time series. Problems solving: multivariate time series forecasting, system response to shocks in econometrics, causal inference studies.

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