Course10610623671 lectures
Introduction to Machine Learning (Tamil)
Prof. Arun Rajkumar · IIT Madras
About
A short course introducing the main ideas and algorithms of Machine Learning. The goal of the course is to cover the topics at a high level so that it would act as a first course for a full fledged Machine Learning course. The course will be delivered in spoken Tamil while the technical terms discussed/exams/assignments will be in English.
Intended audience: Native Tamil speaking audience (Teachers/ Undergraduate Students)
Prerequisites: A basic course in linear algebra/probability/programming/algorithms would be a big plus. These are not strong pre-requisites.
Lectures
Week 1
- Paradigms of Machine Learning
- Few more examples
- Types of Learning
- Types of supervised learning
- Mathematical tools
- Three Fundamental spaces
- Conditional Probability
- Bayes Theorem
- Continuous Probability
Week 2
- Introduction to vectors
- Span of vectors
- Linear Independence
- Basis of vector space
- Orthogonality and Projection
- Introduction to Regression
- Linear regression
- Geometrical Interpretation
- Visual Guide to Orthogonal Projection (Optional)
Week 3
- Iterative solution: Gradient descent
- Gradient Descent
- Choosing Step size
- Taylor Series
- Stochastic Gradient Descent and basis functions
- Regularization Techniques
- Basics of Python
- Basics of numpy
- Gradient Descent using python
Week 4
- Binary Classification
- K-Nearest Neighbour Classification
- Distance metric and Cross-Validation
- Computational efficiency of KNN
- Introduction to Decision Trees
- Level splitting
- Measure of Impurity
- Entropy and Information Gain
- Generative vs Discriminative models
- Naive Bayes classifier
- Conditional Independence
- Classifying the test point and summary
- Tutorial-1-KNN
- Tutorial-2-Decision-Tree
- Tutorial-3-Naive-Bayes
Week 5
- Discriminative models
- Logistic Regression
- Summary and big picture
- Maximum likelihood estimation
- Linear separability
- Perceptron and its learning algorithm
- Perceptron : A thing of past
- Logistic Regression Tutorial
- Perceptron Tutorial
Week 6
Week 7
- Introduction to Ensemble methods
- Bagging
- Bootstrapping
- Limitations of bagging
- Introduction to boosting
- Ada boost
Week 8
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Madras
- Department
- Computer Science and Engineering
- Teacher
- Prof. Arun Rajkumar
- 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.
- 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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