Lecture 1 - Overview of Function Approximation
Lecture 2 - Recap of Probability Theory - 1, Part 1
Lecture 3 - Recap of Probability Theory - 1, Part 2
Lecture 4 - Recap of Probability Theory - 1, Part 3
Lecture 5 - Recap of Probability Theory - Part 2
Lecture 6 - Understanding a Chest X-Ray as Sample from Distribution
Lecture 7 - IID Assumption
Lecture 8 - Distribution Estimation
Lecture 9 - Density Function
Lecture 10 - Challenge With ML
Lecture 11 - Tutorial 1 : Introduction to Python Basics
Lecture 12 - Tutorial 2 : Simple Problem solving in Probability Theory
Lecture 13 - Entropy
Lecture 14 - Kullback-Leibler (KL) Divergence
Lecture 15 - Minimization of KL Divergence
Lecture 16 - Example of ML Estimate
Lecture 17 - Risk Minimization Framework
Lecture 18 - Bayes Classifier
Lecture 19 - Tutorial 3 : Risk Minimization Framework
Lecture 20 - MLE for Gaussian Distribution
Lecture 21 - MLE for Generalized Discrete Random Variable
Lecture 22 - Density Estimation for Mixed Distribution
Lecture 23 - Latent Variable Models
Lecture 24 - MLE for Latent Variable Models
Lecture 25 - Expectation Maximization Algorithm
Lecture 26 - Tutorial 4 : Minmax Classifier
Lecture 27 - Tutorial 5 : Neyman Pearson Classifier
Lecture 28 - Tutorial 6 : Example of NP Classifier, ROC Curve
Lecture 29 - Tutorial 7A : MLE for Gaussian Distribution
Lecture 30 - Tutorial 7B : MLE for Generalized Discrete Distribution
Lecture 31 - Convergence of EM
Lecture 32 - EM for GMMs
Lecture 33 - MAP Estimate
Lecture 34 - Parzen Window
Lecture 35 - Nearest Neighbor Classifier
Lecture 36 - Tutorial 8 : Computation of EM for GMMs
Lecture 37 - Tutorial 9 : MAP Estimate
Lecture 38 - Ordinary Least Squares (OLS)
Lecture 39 - Generalized Least Squares (GLS)
Lecture 40 - Linear Models for Classification
Lecture 41 - Bias - Variance Decomposition and Analysis
Lecture 42 - Bias and Variance in Practice
Lecture 43 - Tutorial 10 Part A : Numerical Example on Bayes Classifier
Lecture 44 - Tutorial 10 Part B : Numerical Example on MLE and MAP Estimate
Lecture 45 - Regularization
Lecture 46 - Regularized ERM and MAP Estimate
Lecture 47 - Stochastic Gradient Descent as a Regularizer
Lecture 48 - Max-Margin Classifier and SVM
Lecture 49 - SVM Formulation
Lecture 50 - Dual Function in SVM
Lecture 51 - SVM for Non-Linear Seperable Case
Lecture 52 - SVM with Kernel Function
Lecture 53 - Neural Networks and Universal Approximation Theorem
Lecture 54 - ERM on Neural Networks and Error Backpropagation
Lecture 55 - Local Receptive Field and Parameter Sharing
Lecture 56 - Convolutional Neural Networks (CNNs) as Regularized MLP
Lecture 57 - Recurrent Neural Networks (RNNs)
Lecture 58 - Back Prapogation in RNNs and Vanishing Gradients Problem
Lecture 59 - LSTMs and GRUs
Lecture 60 - Tutorial 11 : Pytorch - Tensors and Data Loaders
Lecture 61 - Tutorial 12 : Pytorch - Building MLP and Auto Grad
Lecture 62 - Tutorial 13 : Pytorch - Training the Model
Lecture 63 - Attention - Part 1
Lecture 64 - Attention - Part 2
Lecture 65 - Multi-Head Attention and Transformer Architecture
Lecture 66 - Positional Embeddings
Lecture 67 - Transfer Learning and Knowledge Distilation
Lecture 68 - SGD, RMS Prop, ADAM : Optimizers
Lecture 69 - Tutorial 14 Part 1 : CNNs
Lecture 70 - Tutorial 14 Part 2 : Transfer Learning using CNNs
Lecture 71 - Tutorial 15 Part 1 : RNNs, LSTMs and GRUs
Lecture 72 - Tutorial 15 Part 2 : Deep RNNs, LSTMs and GRUs
Lecture 73 - Decision Trees and Impurity Measures
Lecture 74 - Regression Trees
Lecture 75 - Ensemble Methods, Bagging and Boosting
Lecture 76 - Gradient Boosting Algorithm
Lecture 77 - Ada-Boosting
Lecture 78 - Cross Validation
Lecture 79 - Un-Supervised Learning
Lecture 80 - K-Means Clustering
Lecture 81 - PCA - Principal Component Analysis
Lecture 82 - NCE - Noise Contrastive Estimation
Lecture 83 - NCE, Info-NCE, SimCLR, JEPA
Lecture 84 - Introduction to Generative Models
Lecture 85 - GAN - Generative Adversarial Networks
Lecture 86 - Variational Auto Encoders : VAEs
Lecture 87 - Introduction to Large Language Models : LLMs
Lecture 88 - Introduction to Reinforcement Learning : RL
