Machine learning

Course Description

Machine Learning is a science of getting machines to learn, more specifically, designing algorithms that allow computers to learn from empirical data. Machine Learning is about extracting useful information from large and complex datasets. ML has been described as the “next internet” due to the belief that it will revolutionize the way things are being done just as the internet revolutionized the way things are being done. It is the most important technology of this era as its applications cuts across all facets of human endeavors including but not limited to engineering, empirical science, autonomous driving, oil and energy, recommendation systems, speech recognition systems, etc. Machine Learning is a key to developing intelligent systems and analyze data in science and engineering. This course introduces the fundamental methods at the core of modern machine learning. It covers theoretical foundations as well as essential algorithms for supervised and unsupervised learning. Classes on theoretical and algorithmic aspects shall be complemented with practical sessions in form of assignments, practical homeworks and real-life course projects. Topics covered include: Algorithmic models of learning, machine learning framework, learning classifiers and regression models, regression and classifiers evaluations measures, decision trees, neural networks and its variants, support vector machines, Bayesian networks, nearest neighbor classifiers, ensemble and hybrid classifiers. Computational learning theory, Dimensionality reduction, feature selection. Clustering, mixture models, k-means clustering, hierarchical clustering, distributional clustering. Reinforcement learning; pattern recognition.

Course ID: ARTI 308