Learn · Mathematical Foundations of Machine Learning
Lecture 59 of 85Week 9 CNNs and RNNs
LSTMs and GRUs
Prof. Prathosh A P · IISc Bangalore
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- Mathematical Foundations of Machine Learning, IISc Bangalore (at NPTEL)
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All the lectures
- Overview of Function Approximation
- Recap of Probability Theory - 1, Part 1
- Recap of Probability Theory - 1, Part 2
- Recap of Probability Theory - 1, Part 3
- Recap of Probability Theory Part 2
- Understanding a Chest X-Ray as Sample from Distribution
- IID Assumption
- Distribution Estimation
- Density Function
- Challenge With ML
- Tutorial 1 : Introduction to Python Basics
- Tutorial 2 : Simple Problem solving in Probability Theory
- Entropy
- Kullback-Leibler (KL) Divergence
- Minimization of KL Divergence
- Example of ML Estimate
- Risk Minimization Framework
- Bayes Classifier
- Tutorial 3 : Risk Minimization Framework
- MLE for Gaussian Distribution
- MLE for Generalized Discrete Random Variable
- Density Estimation for Mixed Distribution
- Latent Variable Models
- MLE for Latent Variable Models
- Expectation Maximization Algorithm
- Tutorial 4 : Minmax Classifier
- Tutorial 5 : Neyman Pearson Classifier
- Tutorial 6 : Example of NP Classifier, ROC Curve
- Tutorial 7A : MLE for Gaussian Distribution
- Tutorial 7B : MLE for Generalized Discrete Distribution
- Convergence of EM
- EM for GMMs
- MAP Estimate
- Parzen Window
- Nearest Neighbor Classifier
- Tutorial 8 : Computation of EM for GMMs
- Tutorial 9 : MAP Estimate
- Ordinary Least Squares (OLS)
- Generalized Least Squares (GLS)
- Linear Models for Classification
- Bias - Variance Decomposition and Analysis
- Bias & Variance in Practice
- Tutorial 10 Part A : Numerical Example on Bayes Classifier
- Tutorial 10 Part B : Numerical Example on MLE and MAP Estimate
- Regularization
- Regularized ERM and MAP Estimate
- Stochastic Gradient Descent as a Regularizer
- Max-Margin Classifier and SVM
- SVM Formulation
- Dual Function in SVM
- SVM for Non-Linear Seperable Case
- SVM with Kernel Function
- Neural Networks and Universal Approximation Theorem
- ERM on Neural Networks and Error Backpropagation
- Local Receptive Field and Parameter Sharing
- Convolutional Neural Networks(CNNs) as Regularized MLP
- Recurrent Neural Networks(RNNs)
- Back Prapogation in RNNs and Vanishing Gradients Problem
- LSTMs and GRUs
- Tutorial 11 : Pytorch - Tensors and Data Loaders
- Tutorial 12 : Pytorch - Building MLP and Auto Grad
- Tutorial 13 : Pytorch - Training the Model
- Attention Part1
- Attention Part2
- Multi-Head Attention and Transformer Architecture
- Positional Embeddings
- Transfer Learning and Knowledge Distilation
- SGD, RMS Prop, ADAM : Optimizers
- Tutorial 14 Part 1 : CNNs
- Tutorial 14 Part 2 : Transfer Learning using CNNs
- Tutorial 15 Part 1 : RNNs, LSTMs and GRUs
- Tutorial 15 Part 2 : Deep RNNs, LSTMs and GRUs
- Decision Trees and Impurity Measures
- Regression Trees
- Ensemble Methods, Bagging and Boosting
- Gradient Boosting Algorithm
- Ada-Boosting
- Cross Validation
- Un-Supervised Learning
- K-Means Clustering
- PCA - Principal Component Analysis
- NCE - Noise Contrastive Estimation
- NCE, Info-NCE, SimCLR, JEPA
- Introduction to Generative Models
- GAN - Generative Adversarial Networks