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Course11310476170 lectures

Introductory Materials Informatics

Prof. Krishanu Biswas and Prof. Pratik Kumar Ray · IIT Kanpur / IIT Ropar

Start with lecture 1 At NPTEL

Lectures

Week 1

  1. Lecture 1(A) : General Introduction (Part 1) Video
  2. Lecture 1(B) : General Introduction (Part 2) Video
  3. Lecture 2(A) : Statistics & Regression (Part 1) Video
  4. Lecture 2(B) : Statistics & Regression (Part 2) Video
  5. Lecture 3(A) : Probability & Classification (Part 1) Video

Week 2

  1. Lecture 3(B) : (Part - 2) Probability & Classification Video
  2. Lecture 4(A) : (Part-1) Data Handling - I Video
  3. Lecture 4(B) : (Part-2) Data Handling - I Video
  4. Lecture 5(A) : (Part-1) Data Handling - II Video
  5. Lecture 5(B) : (Part-2) Data Handling - II Video

Week 3

  1. Lecture 6(A) : Materials Informatics in Action (Part - 1) Video
  2. Lecture 6(B) : Materials Informatics in Action (Part - 2) Video
  3. Lecture 7(A) : The Gradient Descent Methods (Part - 1) Video
  4. Lecture 7(B) : The Gradient Descent Methods (Part - 2) Video
  5. Lecture 8(A) : Regularization and solvers in regression Video

Week 4

  1. Lecture 8(B) : Regularization and solvers in regression Video
  2. Lecture 9(A) : Various Types of Machine Learning (Part-1) Video
  3. Lecture 9(B) : Various Types of Machine Learning (Part-2) Video
  4. Lecture 10(A) : Describing the Problems (Part-1) Video
  5. Lecture 10(B) : Describing the Problems (Part-2) Video

Week 5

  1. L11(A) : Linear Models (Part-1) Video
  2. L11(B) : Linear Models (Part-2) Video
  3. L12(A) : Decisions Tree (Part-1) Video
  4. L12(B) : Decisions Tree (Part-2) Video
  5. L13(A) : Decision Trees (Part-1) Video

Week 6

  1. Lecture 13(B) : Support Vector Machine (part - 2) Video
  2. Lecture 14(A) : Support Vector Machine (part -1) Video
  3. Lecture 14(B) : Support Vector Machine and Neural Networks (part - 2) Video
  4. Lecture 15(A) : Neural Networks (part -1) Video
  5. Lecture 15(B) : Neural Networks (part - 2) Video

Week 7

  1. Lecture 16(A) : Clustering (Part - 1) Video
  2. Lecture 16(B) : Clustering (Part - 2) Video
  3. Lecture 17(A) : Model Selection (Part - 1) Video
  4. Lecture 17(B) : Clustering (Part - 2) Video
  5. Lecture 18(A) : Visualization (Part - 1) Video
  6. Lecture 18(B) : Visualization and transformation (Part - 2) Video

Week 8

  1. Lecture 19(A) - (part 1): Dataset transformations Video
  2. Lecture 19(B) - (part 2): Dataset transformations Video
  3. Lecture 20(A) - (part 1): Data mining Video
  4. Lecture 20(B) - (part 2): Mapping of materials Video
  5. Lecture 21(A) - (part 1) : Data in Materials Informatics Video
  6. Lecture 21(B) - (part 2) : Data in Materials Informatics Video
  7. Lecture 22(A) _ (part 1) : Structure Maps Video
  8. Lecture 22(B) - (part 2) : Structure Maps Video

Week 9

  1. Lecture -23 (Part 1) - Phase Diagrams Video
  2. Lecture -23 (Part 2) - Periodic Table and Elemental Descriptors Video
  3. Lecture -24 (Part 1) - Featurization - Physical Principles Video
  4. Lecture -24 (Part 2) - Featurization - Pair Plots and Correlation Matrix Video
  5. Lecture 25 - Thermodynamic Features - Miedema’s Model Video
  6. Lecture 26 - ( part 1) : Structure of Materials Video

Week 10

  1. Lecture -26 (part 2): Structure of Materials - Microstructure Video
  2. Lecture -27 (part 1): Prediction of Composition Based Properties - I Video
  3. Lecture -27 (part 2): Prediction of Composition Based Properties - II Video
  4. Lec-28 (part 1): Molecular Fingerprints - I Video
  5. Lec-28 (part 2): Molecular Fingerprints - II Video
  6. Lecture -29 (part 1): Mathematical Microstructure Video
  7. Lecture -29 (part 2): The Microstructure Function Video

Week 11

  1. Lecture 30 - Two-point Statistics and Dimensionality Reduction Video
  2. Lecture 31 A - Combinatorial-Materials-Science Video
  3. Lecture 31 B - Convolutional Neural Network Video
  4. Lecture 32 - Microstructure Representation-Synthetic Microstructures Video
  5. Lecture 33 - P-S-P Linkage Video
  6. Lecture 34 - Decision Making and Interpretation Video

Week 12

  1. Lecture 35 Part 1: (Setting up ML problems) Video
  2. Lecture 35 Part 2: (Interrogating Machine Learning models - Examples) Video
  3. Lecture 36: (Physically Informed Neural Networks) Video
  4. Lecture 37: (Combinatorial Materials Processing) Video
  5. Lecture 38: (High throughput Characterization) Video
  6. Lecture 39: (Synthetic Data) Video
  7. Lecture 40: (Conclusion, Challenges and Future Directions) Video

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Kanpur / IIT Ropar
Department
Metallurgy and Material Science
Taught by
Prof. Krishanu Biswas, Prof. Pratik Kumar Ray
Level
Undergraduate
Language
English
Subjects
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. Coordinated by IIT Kanpur.
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