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Course10610884185 lectures

Mathematical Foundations of Machine Learning

Prof. Prathosh A P · IISc Bangalore

Start with lecture 1 At NPTEL

Lectures

Week 1 Introduction

  1. Overview of Function Approximation Video
  2. Recap of Probability Theory - 1, Part 1 Video
  3. Recap of Probability Theory - 1, Part 2 Video
  4. Recap of Probability Theory - 1, Part 3 Video

Week 2 Setting Up the ML Problem

  1. Recap of Probability Theory Part 2 Video
  2. Understanding a Chest X-Ray as Sample from Distribution Video
  3. IID Assumption Video
  4. Distribution Estimation Video
  5. Density Function Video
  6. Challenge With ML Video
  7. Tutorial 1 : Introduction to Python Basics Video
  8. Tutorial 2 : Simple Problem solving in Probability Theory Video

Week 3 ERM and MLE

  1. Entropy Video
  2. Kullback-Leibler (KL) Divergence Video
  3. Minimization of KL Divergence Video
  4. Example of ML Estimate Video
  5. Risk Minimization Framework Video
  6. Bayes Classifier Video
  7. Tutorial 3 : Risk Minimization Framework Video

Week 4 MLE and EM

  1. MLE for Gaussian Distribution Video
  2. MLE for Generalized Discrete Random Variable Video
  3. Density Estimation for Mixed Distribution Video
  4. Latent Variable Models Video
  5. MLE for Latent Variable Models Video
  6. Expectation Maximization Algorithm Video
  7. Tutorial 4 : Minmax Classifier Video
  8. Tutorial 5 : Neyman Pearson Classifier Video
  9. Tutorial 6 : Example of NP Classifier, ROC Curve Video
  10. Tutorial 7A : MLE for Gaussian Distribution Video
  11. Tutorial 7B : MLE for Generalized Discrete Distribution Video

Week 5 EM, MAP Estimates, Non Parametric Density Estimates

  1. Convergence of EM Video
  2. EM for GMMs Video
  3. MAP Estimate Video
  4. Parzen Window Video
  5. Nearest Neighbor Classifier Video
  6. Tutorial 8 : Computation of EM for GMMs Video
  7. Tutorial 9 : MAP Estimate Video

Week 6 Linear Family of Models and Bias - Variance Analysis

  1. Ordinary Least Squares (OLS) Video
  2. Generalized Least Squares (GLS) Video
  3. Linear Models for Classification Video
  4. Bias - Variance Decomposition and Analysis Video
  5. Bias & Variance in Practice Video
  6. Tutorial 10 Part A : Numerical Example on Bayes Classifier Video
  7. Tutorial 10 Part B : Numerical Example on MLE and MAP Estimate Video

Week 7 Regularization , SVM

  1. Regularization Video
  2. Regularized ERM and MAP Estimate Video
  3. Stochastic Gradient Descent as a Regularizer Video
  4. Max-Margin Classifier and SVM Video
  5. SVM Formulation Video

Week 8 SVM and Neural Networks

  1. Dual Function in SVM Video
  2. SVM for Non-Linear Seperable Case Video
  3. SVM with Kernel Function Video
  4. Neural Networks and Universal Approximation Theorem Video
  5. ERM on Neural Networks and Error Backpropagation Video

Week 9 CNNs and RNNs

  1. Local Receptive Field and Parameter Sharing Video
  2. Convolutional Neural Networks(CNNs) as Regularized MLP Video
  3. Recurrent Neural Networks(RNNs) Video
  4. Back Prapogation in RNNs and Vanishing Gradients Problem Video
  5. LSTMs and GRUs Video
  6. Tutorial 11 : Pytorch - Tensors and Data Loaders Video
  7. Tutorial 12 : Pytorch - Building MLP and Auto Grad Video
  8. Tutorial 13 : Pytorch - Training the Model Video

Week 10 Transformers

  1. Attention Part1 Video
  2. Attention Part2 Video
  3. Multi-Head Attention and Transformer Architecture Video
  4. Positional Embeddings Video
  5. Transfer Learning and Knowledge Distilation Video
  6. SGD, RMS Prop, ADAM : Optimizers Video
  7. Tutorial 14 Part 1 : CNNs Video
  8. Tutorial 14 Part 2 : Transfer Learning using CNNs Video
  9. Tutorial 15 Part 1 : RNNs, LSTMs and GRUs Video
  10. Tutorial 15 Part 2 : Deep RNNs, LSTMs and GRUs Video

Week 11 Classification & Regression Trees

  1. Decision Trees and Impurity Measures Video
  2. Regression Trees Video
  3. Ensemble Methods, Bagging and Boosting Video
  4. Gradient Boosting Algorithm Video

Week 12 Trees and Un-Supervised Learning

  1. Ada-Boosting Video
  2. Cross Validation Video
  3. Un-Supervised Learning Video
  4. K-Means Clustering Video
  5. PCA - Principal Component Analysis Video

Week 13 Self-Supervised Learning and GAN

  1. NCE - Noise Contrastive Estimation Video
  2. NCE, Info-NCE, SimCLR, JEPA Video
  3. Introduction to Generative Models Video
  4. GAN - Generative Adversarial Networks Video

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IISc Bangalore
Department
Computer Science and Engineering
Teacher
Prof. Prathosh A P
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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