Course10610624536 lectures
Optimisation for Machine Learning: Theory and Implementation (Hindi)
Prof. Pravesh Biyani · IIIT Delhi
Lectures
Sample Programming assignment
- Basics of Linear Algebra: Linear Independence
- Linear Algebra: Rank of a matrix
- Linear Algebra - Subspaces of a matrix -1
- Linear Algebra - Subspaces of a matrix -2
- Linear Algebra -- Null space
- Linear Algebra -- Eigen Vectors/Values of a matrix -1
- Linear Algebra -- Eigen Vectors/Values of a matrix -2
Week 0
- Programming Eigen Decomposition using Python
- Singular Value Decomposition - 1
- Singular Value Decomposition - 2
week-03
- Principal Component Analysis - 1
- Principal Component Analysis - 2
- Principal Component Analysis - 3
- Principal Component Analysis - Coding
- Machine Learning - Overview
- Optimisation Problems
week-04
- Gradient of a Vector Valued Function -1
- Gradient of a Vector Valued Function -2
- Neural Netowrks - Overview
- Neural Netowrks - Backpropagation
week-05
- Optimisation - Introduction to optimisation problems
- Optimisation - Relaxation and approximate convergence
- Optimisation - First Order Optimality Condition
- Optimisation - Second Order Optimality Condition
week-06
- Proof of Second Order Optimality Condition, Gradient Methods
- Gradient Descent -2
- Variants of Gradient Descent -1
- Variants of Gradient Descent -2
week-07
week-08
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIIT Delhi
- Department
- Computer Science and Engineering
- Teacher
- Prof. Pravesh Biyani
- 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. Coordinated by IIT Madras.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed
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