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AI Papers

Introduction to Artificial Intelligence (WSI)

I gained deep insights into the field of Artificial Intelligence through hands-on implementations and practical problem-solving. The papers below document this journey and are the direct result of my coursework and exploration, covering fundamentals like Genetic Algorithms, Gradient Descent, Minimax, Neural Networks, and ID3 decision trees.

Papers

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Genetic Algorithm

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Coursework report documenting a Genetic Algorithm solution: problem setup, encoding, selection, crossover/mutation choices, and experimental results with parameter tuning.

AI Evolutionary Optimization

Gradient Descent (Spatial Search)

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Coursework report focused on gradient-based optimization: objective definition, update rule, step-size considerations, convergence behavior, and practical results on a spatial search/optimization task.

AI Optimization Search

Minimax Algorithm (Deterministic Two-Player Games)

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Coursework report describing Minimax for deterministic two-player games: game tree search, evaluation strategy, and experiments demonstrating decision quality and search depth trade-offs.

AI Game AI Search

Neural Networks

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Coursework report on Neural Networks: architecture, activation functions, backpropagation, and training evaluation on classification datasets.

AI ML Neural Networks

ID3 Algorithm (Decision Trees)

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Coursework report on ID3 decision trees: entropy/information gain, tree construction, and evaluation on a classification task with discussion of strengths and limitations.

AI ML Decision Trees