Machine Learning for Core Engineering Disciplines

Watch and track your favorite playlist.

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

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Currently Playing: Lec 25 Gradient Descent Applied to Toy Quadratic Regression

Gradient Descent, Quadratic Regression, MSE Loss, Learning Rate

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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