# InfoCoBuild

## Data Science for Engineers

Data Science for Engineers. Instructors: Prof. Raghunathan Rengaswamy and Prof. Shankar Narasimhan, Department of Computer Science and Engineering, IIT Madras. This course will provide an introduction to data analysis for beginners; a framework to understand different data analysis algorithms; a structured approach to convert high level data analysis problem statements into a well-defined workflow for solution; an introduction to R as a programming language with an emphasis on commands required for this course material; a brief description of concepts in linear algebra and statistics that the participants should focus on; conceptual description of selected machine learning algorithms; practical demonstration of the algorithm through a case study with R. (from nptel.ac.in)

 Lecture 15 - Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors

Go to the Course Home or watch other lectures:

 Lecture 01 - Course Philosophy and Expectation Lecture 02 - Introduction to R (Programming Language) Lecture 03 - Introduction to R (cont.) Lecture 04 - Variables and Datatypes in R Lecture 05 - Data Frames Lecture 06 - Recasting and Joining of Dataframes Lecture 07 - Arithmetic, Logical and Matrix Operations in R Lecture 08 - Advanced Programming in R: Functions Lecture 09 - Advanced Programming in R: Functions (cont.) Lecture 10 - Control Structures Lecture 11 - Data Visualization in R Basic Graphics Lecture 12 - Linear Algebra for Data Science Lecture 13 - Solving Linear Equations Lecture 14 - Solving Linear Equations (cont.) Lecture 15 - Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors Lecture 16 - Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors (cont.) Lecture 17 - Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors (cont.) Lecture 18 - Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors (cont.) Lecture 19 - Statistical Modeling Lecture 20 - Random Variables and Probability Mass/Density Functions Lecture 21 - Sample Statistics Lecture 22 - Hypothesis Testing Lecture 23 - Optimization for Data Science Lecture 24 - Unconstrained Multivariate Optimization Lecture 25 - Unconstrained Multivariate Optimization (cont.) Lecture 26 - Numerical Example: Gradient (Steepest) Descent (OR) Learning Rule Lecture 27 - Multivariate Optimization with Equality Constraints Lecture 28 - Multivariate Optimization with Inequality Constraints Lecture 29 - Introduction to Data Science Lecture 30 - Solving Data Analysis Problems - A Guided Thought Process Lecture 31 - Module: Predictive Modeling Lecture 32 - Linear Regression Lecture 33 - Model Assessment Lecture 34 - Diagnostics to Improve Linear Model Fit Lecture 35 - Simple Linear Regression Model Building Lecture 36 - Simple Linear Regression Model Assessment Lecture 37 - Simple Linear Regression Model Assessment (cont.) Lecture 38 - Multiple Linear Regression Lecture 39 - Cross Validation Lecture 40 - Multiple Linear Regression Modeling Building and Section Lecture 41 - Classification Lecture 42 - Logistic Regression Lecture 43 - Logistic Regression (cont.) Lecture 44 - Performance Measures Lecture 45 - Logistic Regression Implementation in R Lecture 46 - K-Nearest Neighbors (K-NN) Lecture 47 - K-Nearest Neighbors Implementation in R Lecture 48 - K-Means Clustering Lecture 49 - K-Means Implementation in R Lecture 50 - Summary