Course10510500161 lectures
Statistical and Machine Learning Methods: Basics and Applications
Prof. Rajib Maity · IIT Kharagpur
About
The course addresses the challenges often encountered when determining the characteristics of observational data and events. Statistical methods and Machine learning methods play a vital role in understanding the data characteristics and leading to data-driven solutions for many engineering challenges. Initially, the course begins with the basics of probability and statistics related to observations and events, and subsequently models them through random variables. A brief introduction to joint distribution (modelling) through copula is also included in the course for the modelling of joint distribution. The course also covers the joint and conditional distribution of events, which eventually leads to formulating probabilistic solutions for existing challenges across the domain. Furthermore, an introduction to machine learning models and their working principles will help in understanding the suitability of data-driven models and assessing their performance.
Intended audience: Academician, students, Industrial Personnel
Prerequisites: Basic Knowledge of Probability and Statistics
Lectures
Week 1
- Lecture 01 : Introduction to Statistical and Machine Learning
- Lecture 02 : Role of Data Science in Statistical and ML Approach
- Lecture 03 : Role of Statistical and Machine Learning Methods in Interdisciplinary studies
- Lecture 04 : Data Type and Related Issues
- Lecture 05 : Uncertainty and Data Visualization
Week 2
- Lecture 06 : Introduction to Random Variables and Probability Concepts
- Lecture 07 : Introduction to Random Variables and Probability Concepts
- Lecture 08 : Descriptive Statistics
- Lecture 09 : Examples of Total Probability Theorem and Bayes’ Theorem
- Lecture 10 : Moments and Expectation
Week 3
- Lecture 11 : Discrete Probability Distributions
- Lecture 12 : Discrete Probability Distributions…contd.
- Lecture 13 : Continuous Probability Distributions –Part I
- Lecture 14 : Continuous Probability Distributions –Part II
- Lecture 15 : Continuous Probability Distributions – Part III
Week 4
- Lecture 16: Bivariate Joint Probability Distributions
- Lecture 17 : Marginal Probability Distributions for Discrete RV
- Lecture 18 : Marginal Probability Distributions for Continuous RV
- Lecture 19 : Conditional Probability Distributions
- Lecture 20 : Independence between Random Variables
Week 5
- Lecture 21 : Preliminary Concepts of Copula and Sklar’s Theorem
- Lecture 22 : Basic Properties of Copula Functions
- Lecture 23 : Non-parametric Measures of Association
- Lecture 24 : Commonly used Copula Functions and Selection of Best-fit Copula
- Lecture 25 : Applications of Copulas
Week 6
- Lecture 26: Simple Linear Regression
- Lecture 27: Curvilinear Regression
- Lecture 28: Multiple Linear Regression
- Lecture 29: Evaluation of Regression Model
- Lecture 30: Uncertainty Quantification of Regression Model
Week 7
- Lecture 31 : Principal Component Analysis
- Lecture 32 : Selection of Principal Components
- Lecture 33 : Empirical Orthogonal Function
- Lecture 34 : Analysis of Variance-I
- Lecture 35 : Analysis of Variance-II
Week 8
- Lecture 36: Introduction to Statistical Learning
- Lecture 37: Supervised and Unsupervised Learning
- Lecture 38: Model Performance Evaluation
- Lecture 39: Concept of Regularization
- Lecture 40: Concept of Regularization
Week 9
- Lecture 41 : Introduction to Neural Networks
- Lecture 42 : Further details on Neural Networks
- Lecture 43 : Deep Learning
- Lecture 44 : Deep Learning for Time Series
- Lecture 45 : Hybrid Convolution and LSTM
Week 10
- Lecture 46 : Decision Trees: Part I
- Lecture 47 : Decision Trees: Part - II
- Lecture 48 : Random Forest - Part I
- Lecture 49 : Random Forest - Part II
- Lecture 50 : Gradient Boosting
Week 11
- Lecture 51 : Support Vector Machines – Part I
- Lecture 52 : Support Vector Machines – Part II
- Lecture 53 : Support Vector Machines – Part III
- Lecture 54 : Clustering – Part I
- Lecture 55 : Clustering – Part II
- Lecture 56 : Clustering – Part III
Week 12
- Lecture 57 : K-Nearest Neighbours (KNN)
- Lecture 58 : Support Vector Regression for Monthly Streamflow Prediction
- Lecture 59 : Global Climate Fields to Regional Precipitation Anomalies
- Lecture 60 : Potential of Deep Learning in Drought Assessment
- Lecture 61 : Deep Learning Application in a Climate Change Impact Study
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Kharagpur
- Department
- Civil Engineering
- Teacher
- Prof. Rajib Maity
- Level
- Undergraduate
- Language
- English
- Subjects
- Engineering
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed