Course10610624755 lectures
Machine Learning (Ml) In Hindi
Prof. Anubha Gupta · IIIT Delhi
About
This is an introductory course on Machine Learning (ML) that is offered to undergraduate and graduate students. The contents are designed to cover both theoretical and practical aspects of several well-established ML techniques. The assignments will contain theory and programming questions that help strengthen the theoretical foundations as well as learn how to engineer ML solutions to work on simulated and publicly available real datasets. The project(s) will require students to develop a complete Machine Learning solution requiring preprocessing, design of the classifier/regressor, training and validation, testing, and evaluation with quantitative performance comparisons. Each week’s theory contents will be accompanied with a tutorial on python.
Intended audience: Senior UG and PG Students
Prerequisites: Mandatory Prerequisites:1. Programming (Python)2.Matrix calculus3.Probability Theory Desirable Prerequisites:1. Linear Algebra
Lectures
Week 1
- Introduction to Machine Learning
- Linear Algebra: Review (Vector Spaces)
- Linear Algebra: Review (Matrices)
- Probability Theory: Review (Basics of Probability)
Week 2
- Probability Theory: Review (Random Variables)
- Linear Regression
- Linear Regression
- Tutorial: Linear Regression
Week 3
- Linear Regression
- Linear Kernel Regression
- k-Nearest Neighbour (k-NN) Regression
- Tutorial: k-NN Regression
- Tutorial: Kernel Regression
Week 4
- Logistic Regression: Classification Evaluation Metrics
- Logistic Regression
- Logistic Regression: Examples
- Tutorial: Logistic Regression
Week 5
Week 6
- Practical Machine Learning
- Practical Machine Learning - Part 2
- Practical Machine Learning - Part 3
- Practical Machine Learning - Part 4
Week 7
Week 8
- Kernel Support Vector Machines (k-SVM)
- Naïve Bayes Classification
- Decision Trees - Part 1
- Decision Trees - Part 2
- Tutorial: Naïve Bayes Classification
- Tutorial: Decision Trees
Week 9
- k-NN Classifier
- Ensemble Learning
- Random Forests
- Bagging (Bootstrap AGGregatING)
- Tutorial: Random Forests
- Tutorial: k-NN Classifier & Bootstrap AGGregatING (Bagging)
Week 10
Week 11
- Spectral Clustering
- Mixture of Models (Gaussian Mixture Models-GMM)
- Dimensionality Reduction: Principal Component Analysis (PCA) & kernel PCA
- Tutorial: k-means and Spectral Clustering
- Tutorial: Principal Component Analysis (PCA) and Gaussian Mixture Models (GMM)
Week 12
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIIT Delhi
- Department
- Computer Science and Engineering
- Teacher
- Prof. Anubha Gupta
- 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