Machine Learning

Introduction

This course provides a comprehensive introduction to machine learning and pattern recognition. The curriculum examines core statistical and algorithmic methodologies designed to predict outcomes, classify entities, identify clustering patterns, and generate synthetic data. Students will develop the capability to critically evaluate and rigorously apply these techniques to solve forecasting and analytical problems. Mastery of this material equips candidates to conduct independent applied data science work or proceed to advanced studies in the field.

Course content

• Key machine learning algorithms for regression, classification, and clustering, such as hierarchical clustering and K-means.
• Linear discriminant functions and the perceptron model
• Principles of artificial neural networks, such as the multilayer perceptron model, activation functions, and optimization techniques. 
• Generative models such as autoencoders, variational autoencoders.