NPTEL (National Programme on Technology Enhanced Learning) is a Govt. aided
platform to provide Higher Education to all. There are lots of different courses
in which students can enroll and learn Technological skills both theoretical as
well as Practical knowledge. In this blog we are going to discuss about the
course "Introduction to Machine Learning".
ABOUT THE COURSE:
With the increased availability of data from varied sources there has been
increasing attention paid to the various data driven disciplines such as
analytics and machine learning. In this course we intend to introduce some
of the basic concepts of machine learning from a mathematically well
motivated perspective. We will cover the different learning paradigms and
some of the more popular algorithms and architectures used in each of these
paradigms.
|
| Introduction to Machine Learning |
COURSE CURRICULUM:
| Week | Topics Covered |
|---|---|
| 00 | Probability Theory, Linear Algebra, Convex Optimization - (Recap) |
| 01 | Introduction: Statistical Decision Theory - Regression, Classification, Bias Variance |
| 02 | Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares |
| 03 | Linear Classification, Logistic Regression, Linear Discriminant Analysis |
| 04 | Perceptron, Support Vector Machines |
| 05 | Neural Networks - Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation - MLE, MAP, Bayesian Estimation |
| 06 | Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees - Instability Evaluation Measures |
| 07 | Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods - Bagging, Committee Machines and Stacking, Boosting |
| 08 | Gradient Boosting, Random Forests, Multi-class Classification, Naive Bayes, Bayesian Networks |
| 09 | Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation |
| 10 | Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering |
| 11 | Gaussian Mixture Models, Expectation Maximization |
| 12 | Learning Theory, Introduction to Reinforcement Learning, Optional videos (RL framework, TD learning, Solution Methods, Applications) |
Assignment Answer
| Sl No | Week No | Answer |
|---|---|---|
| 1 | Week 1 | Click here |
| 2 | Week 2 | Click here |
| 3 | Week 3 | Click here |
| 4 | Week 4 | Click here |
| 5 | Week 5 | Click here |
| 6 | Week 6 | Click here |
| 7 | Week 7 | Click here |
| 8 | Week 8 | Click here |
| 9 | Week 9 | Click here |
| 10 | Week 10 | Click here |
| 11 | Week 11 | Click here |
| 12 | Week 12 | Click here |
Criteria to Get a Certificate
Criteria Breakdown:
- Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
- Exam score = 75% of the proctored certification exam score out of 100.
- Final score = Average assignment score + Exam score
Eligibility for Certificate:
You will be eligible for a certificate only if:
- Average assignment score ≥ 10/25 (equivalent to ≥ 40% of the maximum assignment score)
- Exam score ≥ 30/75 (equivalent to ≥ 40% of the maximum exam score)
If one of the 2 criteria is not met, you will not get the certificate even if the Final score ≥ 40/100.