Machine Learning for Core Engineering Disciplines

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Curated by: NPTEL - Indian Institute of Science, Bengaluru (66 videos)

Currently Playing: Lec 22 Training ML Models: Gradient Descent and Hessian Matrix Analysis

Gradient Descent, Hessian Matrix, Eigenvalues, Positive Definite, Optimization, Local Minimum, Saddle Point, Linear Regression, Machine Learning, Taylor Expansion

Tracks in this Playlist

Lec 65 Neural Networks with Tensorflow (Tutorial II)
Lec 64 Neural Networks with Tensorflow (Tutorial I)
Lec 63 Variational Autoencoders and Bayesian Generative Modeling
Lec 62 Graph Neural Networks and Generative AI Fundamentals
Lec 61 Recurrent Neural Networks and Sequential Data Processing
Lec 60 Neural Network Challenges (Gradients, Overfitting) and Logic Gate Implementation
Lec 59 Challenges in Training Neural Networks and their Mitigation
Lec 58 Training an Artificial Neural Network:Forward Propagation,Backpropagation,and Hyperparameters
Lec 57 Mathematical Foundation and Activation Functions of Neural Networks
Lec 56 Overview of Advanced Neural Network Architectures: From CNNs to GANs and GNNs
Lec 55 Introduction to Deep Learning and Neural Networks
Lec 54 Supervised Machine Learning Tutorial with Python (Tutorial III)
Lec 53 Supervised Machine Learning Tutorial with Python (Tutorial II)
Lec 52 Supervised Machine Learning Tutorial with Python (Tutorial I)
Lec 51 Gaussian Process Regression (GPR)
Lec 50 Kernel Ridge Regression (KRR)
Lec 49 Support Vector Regression (SVR)
Lec 48 Mathematics of SVM Margins
Lec 47 Support Vector Machines (SVM)
Lec 46 Decision Stump
Lec 45 Gradient Boosted Decision Trees and Advanced Boosting Libraries
Lec 44 Unsupervised Learning in Python (Tutorial 2)
Lec 43 Unsupervised Learning in Python (Tutorial 1)
Lec 42 Adaptive Boosting (AdaBoost)
Lec 41 Random Forest and Boosting of Decision Trees
Lec 40 Ensemble Learning
Lec 39 Classification using Simple Decision Trees
Lec 38 Decision Trees for Regression and Classification
Lec 37 Decision Trees in Supervised ML
Lec 36 Unsupervised Learning on Toy Datasets
Lec 35 Nonlinear Dimensionality Reduction Techniques -II
Lec 34 Nonlinear Dimensionality Reduction Techniques -I
Lec 33 Advanced Clustering Techniques
Lec 32 Fundamentals of Clustering with K-means
Lec 31 Singular Value Decomposition and PCA
Lec 30 Unsupervised Learning: Principal Component Analysis (PCA)
Lec 29 Tutorial-III (Ridge & LASSO Regression,Cross Validation & Hyperparameter tuning using Python)
Lec 28 Tutorial - II (Linear Regression using Python)
Lec 27 Hyperparameter Tuning for ML Models
Lec 26 Cross Validation in Machine Learning
Lec 25 Gradient Descent Applied to Toy Quadratic Regression
Lec 24 Variants of Stochastic Gradient Descent for ML Model Training
Lec 23 Practical Aspects of ML Model Training and Stochastic Gradient Descent
Lec 22 Training ML Models: Gradient Descent and Hessian Matrix Analysis
Lec 21 Training ML Models: Gradient Descent
Lec 20 Loss Function - IV
Lec 19 Loss Functions - III
Lec 18 Loss Functions - II
Lec 17 Loss Functions - I
Lec 16 Tutorial - I (Introduction to Python)
Lec 15 ROC Analysis and Multiclass Classification
Lec 14 Logistic Regression and Evaluation of Binary Classification Models
Lec 13 Bias-Variance TradeOff in Machine Learning
Lec 12 LASSO and Elastic Net Regularization Techniques
Lec 11 Overfitting, Underfitting, and Ridge Regression
Lec 10 Hypothesis Testing and Confidence Intervals: Z-Test and T-Test
Lec 09 Multivariate Linear Regression and Model Evaluation
Lec 08 Understanding Population and Sample Statistics - II
Lec 07 Understanding Population and Sample Statistics - I
Lec 06 Transforming Random Variables and Their Distributions
Lec 05 Probability Distributions for Discrete and Continuous Random Variables
Lec 04 Probability and Statistical Foundations for Machine Learning - II
Lec 03 Probability and Statistical Foundations for Machine Learning - I
Lec 02 Fundamentals of Machine Learning
Lec 01 Introduction to Data Science, Artificial Intelligence, and Machine Learning
Machine Learning for Core Engineering Disciplines Intro