1. SOLVING TRAVELLING SALESMAN PROBLEM...

• lab 2 - using heaps
• lab 4 - using Evolutive Algorithms
• lab 5 - using Ant Colony Optimization

2. MACHINE LEARNING

• lab 6 - exploring performance metrics (accuracy, precision, recall, mean absolute error, root mean squared error) and loss functions (huber)
• lab 7 - predicting world happiness based on GBP and freedom using LEAST SQUARES METHOD (REGRESSION)
• lab 8 - predicting world happiness based on GBP and freedom using BATCH GRADIENT DESCENT METHOD (REGRESSION)
• lab 9 - flower classification using LOGISTIC REGRESSION (CLASSIFICATION)
• lab 10 - image classification - with or without sepia - using ARTIFICIAL NEURAL NETWORKS
• lab 11 - text processing (feature extraction : Bag of Words) and labeling based on K-MEDOIDS; flower classification based on K-MEANS

3. EXAM

• solving exam problems while experimenting with different sklearn algorithms and native code