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Subject Syllabus

AL-405 - Machine Learning

AIML • SEMESTER-4

Course Modules

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

Scope and limitations Machine learning models Supervised Learning Unsupervised Learning Hypothesis space and inductive bias Evaluation, cross-validation Dimensionality Reduction: Subset Selection, Shrinkage Methods, Principle Components Analysis, Partial Least Squares

From Biology to Simulation Neural network representation Neural Networks as a paradigm for parallel processing Perceptron Learning Training a perceptron Multilayer perceptron Back propagation Algorithm Training & Validation Activation functions Vanishing and Exploding Gradients

Decision Trees Naive Bayes Classification Support vector machines for classification problems Random forest for classification and regression problems Linear regression for regression problems Ordinary Least Squares Regression Logistic Regression

Clustering: k-means, adaptive hierarchical clustering, Gaussian mixture model Optimization Using Evolutionary Techniques Number of Clusters Advanced discussion on clustering Expectation Maximization

Factors, response and strategy of experimentation Guidelines for machine learning experiments Cross-validation and resampling methods Measuring classifier performance Hypothesis testing Comparing multiple algorithms Comparison over multiple datasets