Introduction to Machine Learning

О курсе

This course offers a comprehensive introduction to the principles and techniques of machine learning, focusing on building algorithms that can learn patterns from data to make predictions and informed decisions. It encompasses various kinds of learning, primarily categorized into supervised and unsupervised learning. In supervised learning, models are trained on labeled data for tasks such as regression and classification, while unsupervised learning deals with discovering hidden structures in unlabeled data, such as in clustering. Through practical and theoretical modules, learners will gain a deep understanding of model selection, including the use of cross-validation and evaluation metrics such as accuracy, precision, recall, and F1 score. The course also introduces the Bayesian learning framework, offering a probabilistic perspective on learning through concepts like prior and posterior distributions, Maximum Likelihood (ML) estimation, Maximum a Posteriori (MAP) hypothesis, and the Minimum Description Length (MDL) principle. Key concepts such as the bias-variance tradeoff and regularisation techniques are explored to help students develop models that generalise well to unseen data. Learners will also work with foundational models such as Naive Bayes classifiers, both Gaussian and Multinomial, and linear models like Linear Regression and Logistic Regression, supported by methods like feature scaling and gradient descent. The course further extends to non-linear models using polynomial transformations, as well as decision trees built using criteria like entropy and Gini index, with an emphasis on pruning to control overfitting. Students will also explore instance-based learning methods such as k-Nearest Neighbours (KNN) and advanced classification techniques like Support Vector Machines (SVMs), including the use of kernel functions for handling complex, non-linear data. By the end of the course, students will be equipped with a solid foundation in machine learning theory and practice, preparing them for further study or real-world applications in data science and AI.

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BITS Pilani Instructors Group

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