🔊NPTEL Introduction to Machine Learning – IITKGP Week 8 Quiz Assignment Answers | July 2022
This class gives a concise introduction to the basic concepts in machine learning and popular machine learning algorithms. We will go over the standard and most popular supervised learning algorithms like linear regression, logistic regression, choice trees, k-nearest neighbour, an introduction to Bayesian learning and the naïve Bayes algorithm, help vector equipment and kernels and neural networks with an introduction to Deep Learning. We will also deal with the standard clustering algorithms. Aspect reduction approaches will also be talked about. We will introduce the basics of computational learning theory. In the program we will examine various concerns connected to the application of machine learning algorithms. We will go over hypothesis area, overfitting, bias and variance, tradeoffs involving representational ability and learnability, analysis techniques and cross-validation. The course will be accompanied by arms-on difficulty fixing with programming in Python and some tutorial periods.
~~Study course Structure~~
Week 1: Introduction: Fundamental definitions, forms of finding out, hypothesis place and inductive bias, evaluation, cross-validation
Week 2: Linear regression, Final decision trees, overfitting
Week 3: Occasion dependent finding out, Function reduction, Collaborative filtering based advice
7 days 4: Chance and Bayes discovering
7 days 5: Logistic Regression, Aid Vector Machine, Kernel operate and Kernel SVM
7 days 6: Neural network: Perceptron, multilayer community, backpropagation, introduction to deep neural network
7 days 7: Computational learning principle, PAC learning product, Sample complexity, VC Dimension, Ensemble discovering
Week 8: Clustering: k-implies, adaptive hierarchical clustering, Gaussian combination product
🔴Textbooks and References:
1. Machine Learning. Tom Mitchell. To start with Edition, McGraw- Hill, 1997.
2. Introduction to Machine Learning Edition 2, by Ethem Alpaydinal)
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