Learn · Introduction to Data Analytics
Lecture 39 of 49Week 7 - Association Rule Mining and Big Data
Associative Rule Mining
Prof. Nandan Sudarsanam and Prof. Balaraman Ravindran · IIT Madras
Credits and licence
- From
- Introduction to Data Analytics, 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
- Course Overview
- Course Overview (cont\'d)
- Descriptive Statistics - Graphical Approaches
- Descriptive Statistics - Measures of Central Tendency
- Descriptive Statistics - Measures of Dispersion
- Random Variables and Probability Distributions
- Probability Distributions(cont\'d)
- Probability Distributions(cont\'d)
- Inferential Statistics - Motivation
- Inferential Statistics - Single sample tests
- Two Sample tests
- Type 1 and Type 2 Errors
- Confidence Intervals
- ANOVA and Test of Independence
- Short Introduction to Regression
- Introduction to Machine Learning
- Supervised Learning
- Unsupervised Learning
- Ordinary Least Squares Regression
- Simple and Multiple Regression in Excel and Matlab
- Regularization/ Coefficients Shrinkage
- Data Modelling and Algorithmic Modelling Approaches
- Tutorial on Weka
- Logistic Regression
- Training a Logistic Regression Classifier
- Classification and Regression Trees
- Classification and Regression Trees(cont\'d)
- Bias Variance Dichotomy
- Model Assessment and Selection
- Support Vector Machines
- Support Vector Machines(cont\'d)
- Support Vector Machines for Non Linearly Separable Data
- Support Vector Machines and Kernel Transformations
- Tutorial on Decision Trees
- Ensemble Methods and Random Forests
- Artificial Neural Networks
- Artificial Neural Networks(cont\'d)
- Deep Learning
- Associative Rule Mining
- Association Rule Mining (cont\'d)
- Big Data, A small introduction
- Big Data - A small introduction (cont\'d)
- Clustering Analysis
- Clustering Analysis (cont\'d)
- Introduction to Experimentation and Active Learning
- Introduction to Experimentation and Active Learning(cont\'d)
- An Introduction to Online Learning - Reinforcement Learning
- An Introduction to Online Learning - Reinforcement Learning (cont\'d)
- Summary+ Insights into the Final Exam