Machine Learning, ML

KTH Royal Institute of Technology and NPTEL via Swayam

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Overview

Machine Learning, ML is an in-depth course that covers the essential techniques and algorithms in machine learning. Students will explore both supervised and unsupervised learning, with a focus on popular algorithms such as linear regression, decision trees, k-means clustering, and neural networks. The course also delves into key concepts like model evaluation, overfitting, and feature selection. Practical applications in areas such as predictive modeling, classification, and anomaly detection are covered. By the end of the course, students will be able...

Syllabus

Week 1: Introduction to the Machine Learning courseWeek 2: Characterization of Learning ProblemsWeek 3: Forms of RepresentationWeek 4: Inductive Learning based on Symbolic Representations and Weak TheoriesWeek 5: Learning enabled by Prior TheoriesWeek 6: Machine Learning based Artificial Neural NetworksWeek 7: Tools and Resources + Cognitive Science influencesWeek 8: Examples, demos and exam preparations