Learn · Introduction to Data Analytics(Course sponsored by Aricent)
Lecture 35 of 47Week 6 : Supervised Learning( Regression and Classification Techniques)-II
Artificial Neural Networks(cont\'d)
Prof. Nandan Sudarsanam and Dr. Balaraman Ravindran · IIT Madras
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- Introduction to Data Analytics(Course sponsored by Aricent), 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.
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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(contd)
- Probability Distributions(contd)
- 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
- Logistic Regression
- Training a Logistic Regression Classifier
- Classification and Regression Trees
- Classification and Regression Trees(contd)
- Bias Variance Dichotomy
- Model Assessment and Selection
- Support Vector Machines
- Support Vector Machines(contd)
- Support Vector Machines for Non Linearly Separable Data
- Support Vector Machines and Kernel Transformations
- Ensemble Methods and Random Forests
- Artificial Neural Networks
- Artificial Neural Networks(cont\'d)
- Deep Learning
- Associative Rule Mining
- Association Rule Mining (contd)
- Big Data, A small introduction
- Big Data - A small introduction (contd)
- Clustering Analysis
- Clustering Analysis (contd)
- Introduction to Experimentation and Active Learning
- Introduction to Experimentation and Active Learning(contd)
- An Introduction to Online Learning - Reinforcement Learning
- An Introduction to Online Learning - Reinforcement Learning (contd)
- Summary+ Insights into the Final Exam