Concepts in Machine Learning
24CSM509 · S5 · 4 credits
Official KTU 2024 syllabus subject — study with practice, PYQs and AI tools.
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Why:Foundation module
Why:High exam relevance
Why:Core module
Why:Applied concepts
Why:Standard exam coverage
Module 01Overview of machine learning (7 Hrs)expand
Machine learning paradigms-supervised, semi-supervised, unsupervised, reinforcement learning. Basics of parameter estimation - maximum likelihood estimation(MLE) and maximum a posteriori estimation(MAP). Introduction to Bayesian formulation.
Module 02Supervised Learning (9 Hrs)expand
Regression - Linear regression with one variable, Linear regression with multiple variables, solution using gradient descent algorithm and matrix method, basic idea of overfitting in regression. Linear Methods for Classification- Logistic regression, Perceptron, Naive Bayes, Decision tree algorithm ID3.
Module 03Neural Networks (NN) and Support Vector Machines (SVM) (11 Hrs)expand
NN - Multilayer feed forward network, Activation functions (Sigmoid, ReLU, Tanh), Backpropagation algorithm. SVM - Introduction, Maximum Margin Classification, Mathematics behind Maximum Margin Classification, Maximum Margin linear separators, soft margin SVM classifier, non-linear SVM, Kernels for learning non-linear functions, polynomial kernel, Radial Basis Function(RBF).
Module 04Unsupervised Learning (10 Hrs)expand
Clustering - Similarity measures, Hierarchical Agglomerative Clustering, K-means partitional clustering, Expectation maximization (EM) for soft clustering. Dimensionality reduction – Principal Component Analysis, factor Analysis, Multidimensional scaling, Linear Discriminant Analysis.
Module 05Classification Assessment (8 Hrs)expand
Classification Performance measures - Precision, Recall, Accuracy, F-Measure, Receiver Operating Characteristic Curve(ROC), Area Under Curve(AUC. Bootstrapping, Cross Validation, Ensemble methods, Bias-Variance decomposition. Case Study: Develop a classifier for face detection.
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