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Subject Syllabus

AD-502 - Machine Learning

AIDS · Semester 5

Course Modules

Learn topic-wise with Hyper AI and revise using mapped PYQs.

Introduction to machine learning Machine learning life cycle Types of machine learning systems: supervised and unsupervised learning Batch and online learning Instance-based and model-based learning Scope and limitations Challenges of machine learning Data visualization Hypothesis function and testing Data pre-processing Data augmentation Normalizing data sets Bias-variance tradeoff Relation between AI, ML, DL, and DS

Types of clustering methods: partitioning clustering Distribution model-based clustering Hierarchical clustering Fuzzy clustering BIRCH algorithm CURE algorithm Gaussian mixture models and expectation maximization Parameter estimation: MLE and MAP Applications of clustering

Logistic regression Decision tree classification Neural network K-nearest neighbors (K-NN)Support vector machine Naive Bayes: Gaussian, Multinomial, and Bernoulli Performance measures: confusion matrix Classification accuracy Classification report: precision, recall, F1 score, and support

Introduction to ensemble learning Basic ensemble techniques: max voting, averaging, and weighted average Voting classifiers Bagging and pasting Out-of-bag evaluation Random patches and random subspaces Random forests: Extra-Trees and feature importance Boosting: AdaBoost and Gradient Boosting Stacking

The curse of dimensionality Main approaches for dimensionality reduction: projection and manifold learning PCA: preserving the variance Principal components Projecting down to d dimensions Explained variance ratio Choosing the right number of dimensions PCA for compression Randomized PCA Incremental PCA Kernel PCA: selecting a kernel and tuning hyperparameters Learning theory: PAC and VC model