Learn · Introduction to Machine Learning
Lecture 51 of 71Week 8
Random Forests I
Prof. Balaraman Ravindran · IIT Madras
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- Introduction to Machine Learning, IIT Madras (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Introduction to Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Statistical Decision Theory - Regression
- Statistical Decision Theory - Classification
- Bias - Variance
- Linear Regression
- Multivariate Regression
- Subset Selection 1
- Subset Selection 2
- Shrinkage Methods
- Principal Components Regression
- Partial Least Squares
- Linear Classification
- Logistic Regression
- Linear Discriminant Analysis - I - Introduction
- Linear Discriminant Analysis - II
- Linear Discriminant Analysis - III - Another view of LDA
- Tutorial
- Separating Hyperplane Approaches - Perceptron Learning
- Support Vector Machines I - Formulation
- Support Vector Machines II - Interpretation and Analysis
- SVMs for Linearly Non Separable Data
- SVM Kernels
- Hingle Loss formulation of SVM Objective
- Artificial Neural Networks I - Early Models
- Artificial Neural Networks II - Backpropagation
- Artificial Neural Networks III - Backpropagation Continued
- Artificial Neural Networks IV - Training, Initialization and Validation
- Parameter Estimation I - The Maximum Likelihood Estimate
- Parameter Estimation II - Priors and the MAP estimate
- Parameter Estimation III
- Decision Trees - Introduction
- Regression Trees
- Decision Trees - Stopping Criteria and Pruning
- Decision Trees for Classification - Loss Functions
- Decision Trees - Categorical Attributes
- Decision Trees - Multiway Splits
- Decision Trees - Missing Values, Imputation, Surrogate Splits
- Decision Trees - Instability, Smoothness, Repeated Subtrees
- Decision Trees - Example
- Evaluation and Evaluation Measures I
- Evaluation and Evaluation Measures II - Bootstrapping and Cross Validation
- 2 Class Evaluation Measures
- The ROC Curve
- Minimum Description Length and Exploratory Analysis
- Ensemble Methods - Bagging, Committee Machines and Stacking
- Ensemble Methods - Boosting
- Gradient Boosting
- Random Forests I
- Naive Bayes
- Bayesian Networks
- Multiclass Classification
- Undirected Graphical Models - Introduction and Factorization
- Undirected Graphical Models - Potential Functions
- Hidden Markov Models
- Variable Elimination
- Tree Width and Belief Propagation
- Partitional Clustering
- Hierarchical Clustering
- The BIRCH Algorithm
- The CURE Algorithm
- Density Based Clustering
- Gaussian Mixture Models
- Expectation Maximization
- Expectation Maximization - Continued
- Learning Theory
- Introduction to Reinforcement Learning
- RL Framework and TD Learning (Optional)
- Solution Methods and Applications (Optional)