Learn · Machine Learning for Core Engineering Disciplines
Lecture 42 of 65Week 9 Decision Trees and Ensemble Methods - II
Adaptive Boosting (AdaBoost)
Prof. Ananth Govind Rajan · IISc Bangalore
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- Machine Learning for Core Engineering Disciplines, IISc Bangalore (at NPTEL)
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All the lectures
- Introduction to Data Science, Artificial Intelligence, and Machine Learning
- Fundamentals of Machine Learning
- Probability and Statistical Foundations for Machine Learning - I
- Probability and Statistical Foundations for Machine Learning - II
- Probability Distributions for Discrete and Continuous Random Variables
- Transforming Random Variables and Their Distributions
- Understanding Population and Sample Statistics - I
- Understanding Population and Sample Statistics - II
- Multivariate Linear Regression and Model Evaluation
- Hypothesis Testing and Confidence Intervals: Z-Test and T-Test
- Overfitting, Underfitting, and Ridge Regression
- LASSO and Elastic Net Regularization Techniques
- Bias-Variance TradeOff in Machine Learning
- Logistic Regression and Evaluation of Binary Classification Models
- ROC Analysis and Multiclass Classification
- Tutorial - I (Introduction to Python)
- Loss Functions - I
- Loss Functions - II
- Loss Functions - III
- Loss Function - IV
- Training ML Models: Gradient Descent
- Training ML Models: Gradient Descent and Hessian Matrix Analysis
- Practical Aspects of ML Model Training and Stochastic Gradient Descent
- Variants of Stochastic Gradient Descent for ML Model Training
- Gradient Descent Applied to Toy Quadratic Regression
- Cross Validation in Machine Learning
- Hyperparameter Tuning for ML Models
- Tutorial - II (Linear Regression using Python)
- Tutorial - III (Ridge and LASSO Regression, Cross Validation and Hyperparameter tuning using Python)
- Unsupervised Learning: Principal Component Analysis (PCA)
- Singular Value Decomposition and PCA
- Fundamentals of Clustering with K-means
- Advanced Clustering Techniques
- Nonlinear Dimensionality Reduction Techniques -I
- Nonlinear Dimensionality Reduction Techniques -II
- Unsupervised Learning on Toy Datasets
- Decision Trees in Supervised ML
- Decision Trees for Regression and Classification
- Classification using Simple Decision Trees
- Ensemble Learning
- Random Forest and Boosting of Decision Trees
- Adaptive Boosting (AdaBoost)
- Unsupervised Learning in Python (Tutorial 1)
- Unsupervised Learning in Python (Tutorial 2)
- Gradient Boosted Decision Trees and Advanced Boosting Libraries
- Decision Stump
- Support Vector Machines (SVM)
- Mathematics of SVM Margins
- Support Vector Regression (SVR)
- Kernel Ridge Regression (KRR)
- Gaussian Process Regression (GPR)
- Supervised Machine Learning Tutorial with Python (Tutorial I)
- Supervised Machine Learning Tutorial with Python (Tutorial II)
- Supervised Machine Learning Tutorial with Python (Tutorial III)
- Introduction to Deep Learning and Neural Networks
- Overview of Advanced Neural Network Architectures: From CNNs to GANs and GNNs
- Mathematical Foundation and Activation Functions of Neural Networks
- Training an Artificial Neural Network: Forward Propagation, Backpropagation, and Hyperparameters
- Challenges in Training Neural Networks and their Mitigation
- Neural Network Challenges (Gradients, Overfitting) and Logic Gate Implementation
- Recurrent Neural Networks and Sequential Data Processing
- Graph Neural Networks and Generative AI Fundamentals
- Variational Autoencoders and Bayesian Generative Modeling
- Neural Networks with Tensorflow (Tutorial I)
- Neural Networks with Tensorflow (Tutorial II)