CSE 3-2 ML

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CSE  3-2 ML
S.NoChapters / UnitsDownload Link
1Unit 1Download
2Unit 2Download
3Unit 3Download
4Unit 4Download
5Unit 5Download  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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