NPTEL : ACM Summer School in Data Science (Special Lecture Series)

Co-ordinators : Prof. Murugaiyan Amirthalingam


Lecture 1 - Probability - Part 1

Lecture 2 - Probability - Part 2

Lecture 3 - Probability - Part 3

Lecture 4 - Math Foundation - Part 1

Lecture 5 - Math Foundation - Part 2

Lecture 6 - Math Foundation - Part 3

Lecture 7 - Math Foundation 2 - Part 1

Lecture 8 - Math Foundation 2 - Part 2

Lecture 9 - Math Foundation 2 - Part 3

Lecture 10 - Introduction to probability for Data science - Part 1

Lecture 11 - Introduction to probability for Data science - Part 2

Lecture 12 - Introduction to probability for Data science - Part 3

Lecture 13 - Introduction to Statistics for Data science - Part 1

Lecture 14 - Introduction to Statistics for Data science - Part 2

Lecture 15 - Introduction to Statistics for Data science - Part 3

Lecture 16 - Clustering I - Part 1

Lecture 17 - Clustering I - Part 2

Lecture 18 - Clustering I - Part 3

Lecture 19 - Clustering II - Part 1

Lecture 20 - Clustering II - Part 2

Lecture 21 - Clustering II - Part 3

Lecture 22 - Dimensionality Reduction - Part 1

Lecture 23 - Dimensionality Reduction - Part 2

Lecture 24 - Dimensionality Reduction - Part 3

Lecture 25 - Supervised Learning I - Part 1

Lecture 26 - Supervised Learning I - Part 2

Lecture 27 - Supervised Learning I - Part 3

Lecture 28 - Supervised Learning II - Part 1

Lecture 29 - Supervised Learning II - Part 2

Lecture 30 - Supervised Learning II - Part 3

Lecture 31 - Supervised Learning III - Part 1

Lecture 32 - Supervised Learning III - Part 2

Lecture 33 - Supervised Learning III - Part 3

Lecture 34 - Linear Models For Classification - Part 1

Lecture 35 - Linear Models For Classification - Part 2

Lecture 36 - Linear Models For Classification - Part 3

Lecture 37 - Tree Based Methods - Part 1

Lecture 38 - Tree Based Methods - Part 2

Lecture 39 - SVMs - Part 1

Lecture 40 - SVMs - Part 2

Lecture 41 - SVMs - Part 3

Lecture 42 - Ensemble Methods - Part 1

Lecture 43 - Ensemble Methods - Part 2

Lecture 44 - Ensemble Methods - Part 3

Lecture 45 - Learning Theory - Part 1

Lecture 46 - Learning Theory - Part 2

Lecture 47 - Introduction to Probabilistic Modeling - Part 1

Lecture 48 - Introduction to Probabilistic Modeling - Part 2

Lecture 49 - Introduction to Probabilistic Modeling - Part 3

Lecture 50 - Probabilistic/Bayesian Models for Regression - Part 1

Lecture 51 - Probabilistic/Bayesian Models for Regression - Part 2

Lecture 52 - Probabilistic/Bayesian Models for Regression - Part 3

Lecture 53 - Probabilistic Classification, Latent Variable Models - Part 1

Lecture 54 - Probabilistic Classification, Latent Variable Models - Part 2

Lecture 55 - Probabilistic Classification, Latent Variable Models - Part 3

Lecture 56 - Deep Learning I - Part 1

Lecture 57 - Deep Learning I - Part 2

Lecture 58 - Deep Learning I - Part 3

Lecture 59 - Deep Learning II - Part 1

Lecture 60 - Deep Learning II - Part 2

Lecture 61 - Deep Learning II - Part 3

Lecture 62 - Deep Learning III - Part 1

Lecture 63 - Deep Learning III - Part 2

Lecture 64 - Deep Learning III - Part 3

Lecture 65 - Reinforcement learning I - Part 1

Lecture 66 - Reinforcement learning I - Part 2

Lecture 67 - Reinforcement learning II - Part 1

Lecture 68 - Reinforcement learning II - Part 2

Lecture 69 - Map-Reduce and Spark - Part 1

Lecture 70 - Map-Reduce and Spark - Part 2

Lecture 71 - Map-Reduce and Spark - Part 3

Lecture 72 - Scalable Machine Learning - Part 1

Lecture 73 - Scalable Machine Learning - Part 2