Theoretical Computer Science Courses and Certifications

NPTEL;  IIT Madras

Coding Theory

This course provides an introduction to traditional and modern coding theory. Topics covered include linear block codes, cyclic codes (BCH and RS codes), convolutional codes, turbo codes and low-density parity-check (LDPC) codes.

• YouTube
• 1 day 13 hours
• Self-Paced
• Free Video

The Royal Institution

What Computers Can't Do - With Kevin Buzzard

An accessible exploration of the P vs NP problem, asking whether every quickly verifiable solution can also be found quickly.

• YouTube
• 1 hour 4 minutes
• Self-Paced
• Free Video

Ben Eater

What Is Error Correction? Hamming Codes in Hardware

Build a hardware circuit that uses Hamming codes to correct single-bit errors in encoded data.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

Ben Eater

Checksums and Hamming Distance - Error Detection in Data Transmission

Explore how checksums detect errors, compare their effectiveness, identify the errors they catch, and calculate checksum values using Hamming distance.

• YouTube
• 28 minutes
• Self-Paced
• Free Video

Ben Eater

Making a Computer Turing Complete

See how Turing machines and lambda calculus show that an 8-bit breadboard computer can become Turing complete.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Serrano.Academy

Shannon Entropy and Information Gain

A visual introduction to Shannon entropy through random draws, probability distributions, logarithms, and the average questions needed to identify an outcome.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Reducible

How PNG Works - Compromising Speed for Quality

Explore how PNG uses lossless compression and adaptive filtering, and why the simpler QOI format can achieve comparable compression at much higher speed.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

Reducible

Huffman Codes - An Information Theory Perspective

See how information theory motivates Shannon–Fano coding and Huffman’s optimal, bottom-up compression algorithm through examples and implementation.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Ryan O'Donnell

Undergraduate Computational Complexity Theory

Explore fundamental computational complexity theory through 28 comprehensive lectures covering P vs NP, reductions, space complexity, and randomized algorithms from Carnegie Mellon.

• YouTube
• 1 day 13 hours 36 minutes
• Self-Paced
• Free Video

The Julia Programming Language

Computational Thinking - MIT 18.S191/6.S083 Spring 2021

Master computational thinking through Julia programming while tackling real-world problems in data analysis, mathematical modeling, and climate science with integrated CS concepts.

• YouTube
• 23 hours 24 minutes
• Self-Paced
• Free Video

Complexity Explorer

Maximum Entropy Methods Tutorial

Discover maximum entropy methods for understanding probability distributions through practical examples including taxicab problems and real-world modeling applications.

• YouTube
• 1 hour 46 minutes
• Self-Paced
• Free Video

Data Compression I - Lecture 2: Prefix Free Codes

1 rating

Explore prefix-free codes in data compression, covering theory and applications. Gain insights from Stanford experts on efficient information encoding techniques.

• YouTube
• 1 hour 17 minutes
• Self-Paced
• Free Video

Data Compression I - Course Introduction and Lossless Compression Basics - Lecture 1

Introduction to lossless data compression fundamentals, covering key concepts and techniques for efficient information storage and transmission.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

Data Compression I - Lecture 5: Asymptotic Equipartition Property

Explore the Asymptotic Equipartition Property in data compression, a fundamental concept for understanding information theory and efficient coding techniques.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video

Context-based Arithmetic Coding and LLM Compression - Stanford EE274 Lecture 9

Explore context-based arithmetic coding and compression techniques for large language models in this advanced lecture on data compression theory and applications.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video