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Last updated:
September 26, 2023
Duration:
Unlimited Duration
FREE
This course includes:
Unlimited Duration
Badge on Completion
Certificate of completion
Unlimited Duration
Description
Introduction to Machine Learning. Instructor: Dr. Balaraman Ravindran, Department of Computer Science and Engineering, IIT Madras.
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 (from nptel.ac.in)
Course Curriculum
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- Lecture 01 – A Brief Introduction to Machine Learning Unlimited
- Lecture 02 – Supervised Learning Unlimited
- Lecture 03 – Unsupervised Learning Unlimited
- Lecture 04 – Reinforcement Learning Unlimited
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- Lecture 05 – Probability Basics 1 Unlimited
- Lecture 06 – Probability Basics 2 Unlimited
- Lecture 07 – Linear Algebra 1 Unlimited
- Lecture 08 – Linear Algebra 2 Unlimited
- Lecture 12 – Linear Regression Unlimited
- Lecture 13 – Multivariate Regression Unlimited
- Lecture 19 – Linear Classification Unlimited
- Lecture 20 – Logistic Regression Unlimited
- Lecture 21 – Linear Discriminant Analysis 1 Unlimited
- Lecture 22 – Linear Discriminant Analysis 2 Unlimited
- Lecture 23 – Linear Discriminant Analysis 3 Unlimited
- Lecture 24 – Weka Tutorial Unlimited
- Lecture 26 – Perceptron Learning Unlimited
- Lecture 27 – Support Vector Machines – Formulation Unlimited
- Lecture 28 – Support Vector Machines – Interpretation and Analysis Unlimited
- Lecture 29 – Support Vector Machines for Linearly Non-separable Data Unlimited
- Lecture 30 – SVM Kernels Unlimited
- Lecture 31 – SVM – Hinge Loss Formulation Unlimited
- Lecture 36 – Maximum Likelihood Estimate Unlimited
- Lecture 37 – Priors and the MAP Estimate Unlimited
- Lecture 38 – Bayesian Parameter Estimation Unlimited
- Lecture 48 – Evaluation and Evaluation Measures I Unlimited
- Lecture 49 – Bootstrapping and Cross Validation Unlimited
- Lecture 50 – 2 Class Evaluation Measures Unlimited
- Lecture 51 – The ROC Curve Unlimited
- Lecture 52 – Minimum Description Length and Exploratory Analysis Unlimited
- Lecture 59 – Bagging, Committee Machines and Stacking Unlimited
- Lecture 60 – Boosting Unlimited
- Lecture 61 – Gradient Boosting Unlimited
- Lecture 62 – Random Forests Unlimited
- Lecture 70 – Partitional Clustering Unlimited
- Lecture 71 – Hierarchical Clustering Unlimited
- Lecture 72 – Threshold Graphs Unlimited
- Lecture 73 – The BIRCH Algorithm Unlimited
- Lecture 74 – The CURE Algorithm Unlimited
- Lecture 75 – Density based Clustering Unlimited
- Lecture 79 – Spectral Clustering Unlimited
- Lecture 81 – Frequent Itemset Mining Unlimited
- Lecture 82 – The Apriori Property Unlimited
- Lecture 86 – Multi-class Classification Unlimited
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