What Will You Learn?
In this course, you will delve into the fundamental concepts of machine learning from a mathematically motivated perspective, exploring various learning paradigms and the popular algorithms and architectures within each paradigm. With the increasing availability of data from diverse sources, this course aims to provide a comprehensive understanding of the key principles essential for success in data-driven disciplines such as analytics and machine learning.
About This Course
Provider: NPTEL
Format: Online
Duration: 25 hours to complete [Approx]
Target Audience: Beginners
Learning Objectives: Upon completion, you will have a solid understanding of the fundamental concepts of machine learning, approached from a mathematically well-motivated perspective.
Course Prerequisites: We will assume that the students know programming for some of the assignments.If the students have done introductory courses on probability theory and linear algebra it would be helpful.
Assessment and Certification: Earn a Certificate upon completion from the relevant Provider
Instructor: IIT Madras
Key Topics: Machine Learning, Neural Networks
Topic Covered:
- - Probability Theory, Linear Algebra, Convex Optimization - (Recap)
- - Introduction: Statistical Decision Theory - Regression, Classification, Bias Variance
- - Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares
- - Linear Classification, Logistic Regression, Linear Discriminant Analysis
- - Perceptron, Support Vector Machines
- - Neural Networks - Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation - MLE, MAP, Bayesian Estimation
- - Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees - Instability Evaluation Measures
- - Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods - Bagging, Committee Machines and Stacking, Boosting
- - Gradient Boosting, Random Forests, Multi-class Classification, Naive Bayes, Bayesian Networks
- - Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation
- - Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering
- - Gaussian Mixture Models, Expectation Maximization
- - Learning Theory, Introduction to Reinforcement Learning, Optional videos (RL framework, TD learning, Solution Methods, Applications)
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