Embedded Files
UNIT-I
Introduction to machine learning, Machine learning life cycle, Types of Machine Learning System (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 (Artificial Intelligence), ML (Machine Learning), DL (Deep Learning) and DS (Data Science).
UNIT-II
Clustering in Machine Learning: Types of Clustering Method: Partitioning Clustering, Distribution Model-Based Clustering, Hierarchical Clustering, Fuzzy Clustering. Birch Algorithm, CURE Algorithm. Gaussian Mixture Models and Expectation Maximization. Parameters estimations – MLE, MAP. Applications of Clustering.
UNIT-III
Classification algorithm: - Logistic Regression, Decision Tree Classification, Neural Network, K-Nearest Neighbors (K-NN), Support Vector Machine, Naive Bayes (Gaussian, Multinomial, Bernoulli). Performance Measures: Confusion Matrix, Classification Accuracy, Classification Report: Precisions, Recall, F1 score and Support.
UNIT-IV
Ensemble Learning and Random Forest: Introduction to Ensemble Learning, Basic Ensemble Techniques (Max Voting, Averaging, Weighted Average), Voting Classifiers, Bagging and Pasting, Out-of-Bag Evaluation, Random Patches and Random Subspaces, Random Forests (Extra-Trees, Feature Importance), Boosting (AdaBoost, Gradient Boosting), Stacking.
UNIT-V
Dimensionality Reduction: The Curse of Dimensionality, Main Approaches for Dimensionality Reduction (Projection, 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 Hyper parameters. Learning Theory: PAC and VC model.
Notes
Assignment Session 2025-26 (ODD)( Batch: 2023-2027)
AD-502 Machine Learning _Assignment 1_UNIT-1(2023-2027) Deadline:10-10-2025
AD-502 _Machine Learning _Assignment 2_UNIT-2(2023-2027) Deadline:15/10/2025
AD-502 _Machine Learning _Assignment 3_UNIT-3 (2023-2027) Deadline:25/10/2025
QUIZ Session 2025-26 (ODD) (Batch: 2023-2027)
AD-502 Machine Learning_QUIZ-1 (2023-2027)
AD-502 Machine Learning_QUIZ-2 (2023-2027)
AD-502 Machine Learning_QUIZ-3 (2023-2027)
AD-502 Machine Learning_QUIZ-4 (2023-2027)
AD-502 Machine Learning_QUIZ-5 (2023-2027)
List of Experiments Session 2025-26 (ODD)(Batch: 2023-2027)
Exp-1:
Exp-2:
Exp-3:
Exp-4:
Exp-5:
Exp-6:
Exp-7:
Exp-8:
Exp-9:
Exp-10:
RGPV OLD PAPER
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