Projects from IN3050 - Introduction to Artificial Intelligence and Machine Learning

Assignment 1: Evolutionary Algorithms and the Traveling Salesman Problem

• Description: In this assignment, I explored evolutionary algorithms to address the Traveling Salesman Problem (TSP). I evaluated various optimization techniques, including exhaustive search, hill climbing, and genetic algorithms (GAs). By implementing a GA, I observed how it efficiently optimized tour selection compared to exhaustive methods. Results are visualized using Matplotlib.
• Skills: Optimization algorithms, evolutionary computing, data analysis.

Assignment 2: Supervised Learning

• Description: In this assignment, I focused on implementing and evaluating various machine learning models, including Linear Regression, Logistic Regression, and Multi-Layer Perceptrons (MLP). I explored the importance of hyperparameter tuning and the effects of scaling input data on model performance. Results are visualized using Matplotlib and Pandas.
• Skills: Machine learning, hyperparameter tuning, data scaling, model evaluation.

Assignment 3: Unsupervised Learning

• Description: In this assignment, I focused on unsupervised learning techniques, specifically Principal Component Analysis (PCA) and K-means clustering. I implemented PCA to reduce the dimensionality of datasets, effectively visualizing data structure and identifying significant features. Additionally, I used the K-means clustering algorithm to group data points in the Iris dataset. I experimented with different values of k (the number of clusters), evaluating the clustering results through visualizations and performance metrics.
• Skills: Unsupervised learning, PCA, K-means clustering, data visualization, performance evaluation.