All Materials regarding to B.tech R-20 in Computer Science Engineering with unit wise for Every Subjects are available.
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CSE 3-2 ML | ||
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S.No | Chapters / Units | Download Link |
1 | Unit 1 | Download |
2 | Unit 2 | Download |
3 | Unit 3 | Download |
4 | Unit 4 | Download |
5 | Unit 5 | Download Part B |
CSE ML Important Topics Questions
Unit I:
Introduction- Artificial Intelligence, Machine Learning, Deep learning, Types of Machine Learning
Systems, Main Challenges of Machine Learning.
Statistical Learning: Introduction, Supervised and Unsupervised Learning, Training and Test Loss,
Tradeoffs in Statistical Learning, Estimating Risk Statistics, Sampling distribution of an estimator,
Empirical Risk Minimization.
Unit II:
Supervised Learning(Regression/Classification):Basic Methods: Distance based Methods, Nearest
Neighbours, Decision Trees, Naive Bayes, Linear Models: Linear Regression, Logistic Regression,
Generalized Linear Models, Support Vector Machines, Binary Classification: Multiclass/Structured
outputs, MNIST, Ranking.
Unit III:
Ensemble Learning and Random Forests: Introduction, Voting Classifiers, Bagging and Pasting,
Random Forests, Boosting, Stacking.
Support Vector Machine: Linear SVM Classification, Nonlinear SVM Classification SVM Regression,
Naïve Bayes Classifiers.
Unit IV:
Unsupervised Learning Techniques: Clustering, K-Means, Limits of K-Means, Using Clustering for
Image Segmentation, Using Clustering for Preprocessing, Using Clustering for Semi-Supervised
Learning, DBSCAN, Gaussian Mixtures.
Dimensionality Reduction: The Curse of Dimensionality, Main Approaches for Dimensionality
Reduction, PCA, Using Scikit-Learn, Randomized PCA, Kernel PCA.
Unit V:
Neural Networks and Deep Learning: Introduction to Artificial Neural Networks with Keras,
Implementing MLPs with Keras, Installing TensorFlow 2, Loading and Preprocessing Data with
TensorFlow.
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