Learn · Statistical and Machine Learning Methods: Basics and Applications
Lecture 47 of 61Week 10
Lecture 47 : Decision Trees: Part - II
Prof. Rajib Maity · IIT Kharagpur
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- Statistical and Machine Learning Methods: Basics and Applications, IIT Kharagpur (at NPTEL)
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All the lectures
- Lecture 01 : Introduction to Statistical and Machine Learning
- Lecture 02 : Role of Data Science in Statistical and ML Approach
- Lecture 03 : Role of Statistical and Machine Learning Methods in Interdisciplinary studies
- Lecture 04 : Data Type and Related Issues
- Lecture 05 : Uncertainty and Data Visualization
- Lecture 06 : Introduction to Random Variables and Probability Concepts
- Lecture 07 : Introduction to Random Variables and Probability Concepts
- Lecture 08 : Descriptive Statistics
- Lecture 09 : Examples of Total Probability Theorem and Bayes’ Theorem
- Lecture 10 : Moments and Expectation
- Lecture 11 : Discrete Probability Distributions
- Lecture 12 : Discrete Probability Distributions…contd.
- Lecture 13 : Continuous Probability Distributions –Part I
- Lecture 14 : Continuous Probability Distributions –Part II
- Lecture 15 : Continuous Probability Distributions – Part III
- Lecture 16: Bivariate Joint Probability Distributions
- Lecture 17 : Marginal Probability Distributions for Discrete RV
- Lecture 18 : Marginal Probability Distributions for Continuous RV
- Lecture 19 : Conditional Probability Distributions
- Lecture 20 : Independence between Random Variables
- Lecture 21 : Preliminary Concepts of Copula and Sklar’s Theorem
- Lecture 22 : Basic Properties of Copula Functions
- Lecture 23 : Non-parametric Measures of Association
- Lecture 24 : Commonly used Copula Functions and Selection of Best-fit Copula
- Lecture 25 : Applications of Copulas
- Lecture 26: Simple Linear Regression
- Lecture 27: Curvilinear Regression
- Lecture 28: Multiple Linear Regression
- Lecture 29: Evaluation of Regression Model
- Lecture 30: Uncertainty Quantification of Regression Model
- Lecture 31 : Principal Component Analysis
- Lecture 32 : Selection of Principal Components
- Lecture 33 : Empirical Orthogonal Function
- Lecture 34 : Analysis of Variance-I
- Lecture 35 : Analysis of Variance-II
- Lecture 36: Introduction to Statistical Learning
- Lecture 37: Supervised and Unsupervised Learning
- Lecture 38: Model Performance Evaluation
- Lecture 39: Concept of Regularization
- Lecture 40: Concept of Regularization
- Lecture 41 : Introduction to Neural Networks
- Lecture 42 : Further details on Neural Networks
- Lecture 43 : Deep Learning
- Lecture 44 : Deep Learning for Time Series
- Lecture 45 : Hybrid Convolution and LSTM
- Lecture 46 : Decision Trees: Part I
- Lecture 47 : Decision Trees: Part - II
- Lecture 48 : Random Forest - Part I
- Lecture 49 : Random Forest - Part II
- Lecture 50 : Gradient Boosting
- Lecture 51 : Support Vector Machines – Part I
- Lecture 52 : Support Vector Machines – Part II
- Lecture 53 : Support Vector Machines – Part III
- Lecture 54 : Clustering – Part I
- Lecture 55 : Clustering – Part II
- Lecture 56 : Clustering – Part III
- Lecture 57 : K-Nearest Neighbours (KNN)
- Lecture 58 : Support Vector Regression for Monthly Streamflow Prediction
- Lecture 59 : Global Climate Fields to Regional Precipitation Anomalies
- Lecture 60 : Potential of Deep Learning in Drought Assessment
- Lecture 61 : Deep Learning Application in a Climate Change Impact Study