Prerequisites#

Completion of a basic course in Machine Learning.
(Recommended, but not mandatory) Completion of a course in Deep Learning, or exposure to topics in neural networks.
Knowledge of basics in probability, linear algebra, and calculus.
Experience of programming, preferably in Python.
Basic idea about Image Processing and/or Computer Vision should be helpful.

INTENDED AUDIENCE :Senior Undergraduate Students + Post-Graduate Students.

INDUSTRIES SUPPORT : All companies that use computer vision for their products/services (Microsoft, Google, Facebook, Apple, TCS, Cognizant, L&T, etc)

Relevant Courses offered by NPTEL#

The courses listed below can be audited before, with or after taking/auditing our course.

The courses marked as Recommended are the ones that we highly encourage students to audit before taking our course.

Mathematics#

Essential Mathematics for Machine Learning (Recommended): https://nptel.ac.in/courses/111107137

Linear Algebra#

Matrix Theory: https://nptel.ac.in/courses/111108157
Applied Linear Algebra for Signal Processing, Data Analytics and Machine Learning: https://nptel.ac.in/courses/108104174
Linear Algebra: https://nptel.ac.in/courses/111106135
Matrix Analysis with Applications: https://nptel.ac.in/courses/111107112
Applied Linear Algebra: https://nptel.ac.in/courses/108106171

Calculus#

Calculus of One Real Variable: https://nptel.ac.in/courses/109104124’
Calculus of Several Real Variables: https://nptel.ac.in/courses/111104125
Multivariable Calculus: https://nptel.ac.in/courses/111107108

Probability and Statistics#

Probability for Computer Science: https://nptel.ac.in/courses/106104233
Essentials of Data Science With R Software _ 1: Probability and Statistical Inference: https://nptel.ac.in/courses/111104146
Essentials of Data Science With R Software _ 2: Sampling Theory and Linear Regression Analysis: https://nptel.ac.in/courses/111104147
Advanced Probability Theory: https://nptel.ac.in/courses/111102134
Statistical Inference: https://nptel.ac.in/courses/111102112
Non-parametric Statistical Inference:https://nptel.ac.in/courses/111102143

Optimization#

Applied Optimization for Wireless, Machine Learning, Big Data: https://nptel.ac.in/courses/108104112
Optimization from fundamentals: https://nptel.ac.in/courses/112101298
Constrained and unconstrained optimization: https://nptel.ac.in/courses/111105100
Convex Optimization: https://nptel.ac.in/courses/111104068

Machine Learning#

Machine Learning for Engineering and Science Applications: https://nptel.ac.in/courses/106106198
Introduction to Machine Learning: https://nptel.ac.in/courses/106106139
Practical Machine Learning with Tensorflow: https://nptel.ac.in/courses/106106213

Deep Learning#

Deep Learning - Part 1 (Recommended): https://nptel.ac.in/courses/106106184
Deep Learning - Part 2: https://nptel.ac.in/courses/106106201
Deep Learning For Visual Computing: https://nptel.ac.in/courses/108105103

Python#

Programming, Data Structures and Algorithms using Python: https://nptel.ac.in/courses/106106145
The Joy of Computing using Python: https://nptel.ac.in/courses/106106182
Data Analytics with Python: https://nptel.ac.in/courses/106107220
Python for Data Science: https://nptel.ac.in/courses/106106212

Image Processing#

Image Signal Processing: https://nptel.ac.in/courses/108106168
Digital Image Processing: https://nptel.ac.in/courses/117105135

Computer Vision#

Computer Vision: https://nptel.ac.in/courses/106105216
Computer Vision and Image Processing - Fundamentals and Applications: https://nptel.ac.in/courses/108103174
Our Mathematical Senses Part 1: Geometry of Vision: https://nptel.ac.in/courses/111106156

Deep Learning For Computer Vision

Prerequisites

Contents