Learn · Introduction to Machine Learning (Tamil)
Lecture 29 of 71Week 4
K-Nearest Neighbour Classification
Prof. Arun Rajkumar · IIT Madras
Credits and licence
- From
- Introduction to Machine Learning (Tamil), IIT Madras (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- 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
- 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)
- 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
- 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
- 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
- Support Vector Machine
- Optimizing weights
- Handling Outliers
- Dual Formulation
- Kernel formulation
- Introduction to Ensemble methods
- Bagging
- Bootstrapping
- Limitations of bagging
- Introduction to boosting
- Ada boost
- Unsupervised learning
- K-means Clustering
- LLyod's Algorithms
- Convergence and Initialization
- Representation Learning
- Orthogonal Projection
- Covariance Matrix and Eigen direction
- PCA and mean centering
- Principal Component Analysis - Tutorial