Machine Learning for Core Engineering Disciplines
66 videos ยท NPTEL - Indian Institute of Science, Bengaluru
- 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