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This course provides a challenging introduction to some of the central ideas of theoretical computer science. It attempts to present a vision of “computer science beyond computers” that is, CS as a set of mathematical tools for understanding complex systems such as universes and minds. This course also covers the power of randomness, cryptography and one-way functions and computational theories of learning.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: MIT

Course Curriculum

Module 01
Introduction 00:12:00
Logic 00:10:00
Circuits and finite automata 00:15:00
Turing machines 03:00:00
Reducibility and Gödel 00:10:00
Minds and machines 00:15:00
Complexity 00:10:00
Polynomial time 00:15:00
P and NP 00:15:00
NP-completeness 00:10:00
NP-completeness in practice 00:10:00
Space complexity and more 00:10:00
Module 02
Randomness 00:10:00
Probabilistic complexity classes 00:05:00
Derandomization or cryptography double feature 00:15:00
Private-key cryptography 00:10:00
Public-key cryptography 00:10:00
Cryptographic protocols 00:15:00
Interactive proofs or machine learning 00:10:00
Probably Approximately Correct (PAC) learning 00:05:00
Learning, Chomsky, RSA, quantum 00:05:00
Quantum computing 00:15:00
Quantum algorithms 00:10:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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