Lecture 1 - General Introduction - Part 1
Lecture 2 - General Introduction - Part 2
Lecture 3 - Statistics and Regression - Part 1
Lecture 4 - Statistics and Regression - Part 2
Lecture 5 - Probability and Classification - Part 1
Lecture 6 - Probability and Classification - Part 2
Lecture 7 - Data Handling-I : Part 1
Lecture 8 - Data Handling-I : Part 2
Lecture 9 - Data Handling-II : Part 1
Lecture 10 - Data Handling-II : Part 2
Lecture 11 - Materials Informatics in Action - Part 1
Lecture 12 - Materials Informatics in Action - Part 2
Lecture 13 - The Gradient Descent Methods - Part 1
Lecture 14 - The Gradient Descent Methods - Part 2
Lecture 15 - Regularization and solvers in regression
Lecture 16 - Regularization and solvers in regression
Lecture 17 - Various Types of Machine Learning - Part 1
Lecture 18 - Various Types of Machine Learning - Part 2
Lecture 19 - Describing the Problems - Part 1
Lecture 20 - Describing the Problems - Part 2
Lecture 21 - Linear Models - Part 1
Lecture 22 - Linear Models - Part 2
Lecture 23 - Decisions Tree - Part 1
Lecture 24 - Decisions Tree - Part 2
Lecture 25 - Decision Trees - Part 1
Lecture 26 - Support Vector Machine - Part 1
Lecture 27 - Support Vector Machine - Part 2
Lecture 28 - Support Vector Machine and Neural Networks - Part 2
Lecture 29 - Neural Networks - Part 1
Lecture 30 - Neural Networks - Part 2
Lecture 31 - Clustering - Part 1
Lecture 32 - Clustering - Part 2
Lecture 33 - Model Selection - Part 1
Lecture 34 - Clustering - Part 2
Lecture 35 - Visualization - Part 1
Lecture 36 - Visualization and transformation - Part 2
Lecture 37 - Dataset transformations - Part 1
Lecture 38 - Dataset transformations - Part 2
Lecture 39 - Data mining - Part 1
Lecture 40 - Mapping of materials - Part 2
Lecture 41 - Data in Materials Informatics - Part 1
Lecture 42 - Data in Materials Informatics - Part 2
Lecture 43 - Structure Maps - Part 1
Lecture 44 - Structure Maps - Part 2
Lecture 45 - Phase Diagrams - Part 1
Lecture 46 - Periodic Table and Elemental Descriptors - Part 2
Lecture 47 - Featurization - Physical Principles - Part 1
Lecture 48 - Featurization - Pair Plots and Correlation Matrix - Part 2
Lecture 49 - Thermodynamic Features - Miedema’s Model
Lecture 50 - Structure of Materials - Part 1
Lecture 51 - Structure of Materials - Microstructure - Part 2
Lecture 52 - Prediction of Composition Based Properties - Part I
Lecture 53 - Prediction of Composition Based Properties - Part II
Lecture 54 - Molecular Fingerprints - Part I
Lecture 55 - Molecular Fingerprints - Part II
Lecture 56 - Mathematical Microstructure - Part 1
Lecture 57 - The Microstructure Function - Part 2
Lecture 58 - Two-point Statistics and Dimensionality Reduction
Lecture 59 - Combinatorial-Materials-Science
Lecture 60 - Convolutional Neural Network
Lecture 61 - Microstructure Representation-Synthetic Microstructures
Lecture 62 - P-S-P Linkage
Lecture 63 - Decision Making and Interpretation
Lecture 64 - Setting up ML problems - Part 1
Lecture 65 - Interrogating Machine Learning models - Examples - Part 2
Lecture 66 - Physically Informed Neural Networks
Lecture 67 - Combinatorial Materials Processing
Lecture 68 - High throughput Characterization
Lecture 69 - Synthetic Data
Lecture 70 - Conclusion, Challenges and Future Directions
