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Deep Learning
Implement Stochastic Gradient Descent with Weight Decay and Gradient Accumulation

Clustering Algorithms
K-means with Convergence Checking

Statistical Techniques in ML
Compute Confidence Intervals for ML Evaluation Results Using Bootstrap

Tree-based Models
Implement Decision Tree Classifier with Multiple Pre-Pruning Techniques

Probability
Sampling for Stream Data

Linear Algebra
Matrix Determinant Calculation

Linear Algebra
QR Decomposition Implementation

Data Processing
Implement Training Data Iterator with Class Balancing
