Computer Science Master's Degree - Foundations of Computer Science offered by Columbia University
- Private University
- 1 Campus
- Estd. 1754
Computer Science Master's Degree - Foundations of Computer Science at Columbia University Overview
Duration | 24 months |
Total fee | ₹53.73 Lakh |
Mode of learning | Online |
Course Level | PG Degree |
Computer Science Master's Degree - Foundations of Computer Science at Columbia University Highlights
- Earn a master degree from Columbia University
Computer Science Master's Degree - Foundations of Computer Science at Columbia University Course details
- For students who wish to develop state of the art knowledge of the theoretical foundations of Computer Science
- The theory of computation plays a crucial role in providing solid foundations for all areas of Computer Science, including systems, artificial intelligence, security, and circuit design
- This track will help you develop leading-edge knowledge of theoretical Computer Science and its applications
Computer Science Master's Degree - Foundations of Computer Science at Columbia University Curriculum
Track Course
Analysis of Algorithms I
Introduction to Computational Complexity
Track Program: Electives I
Graph Theory
Combinatorial Theory
Numerical Algorithms and Complexity
Computational Learning Theory
Introduction to Cryptography
Quantum Computing
Track Program: Electives II
Graph Theory
Combinatorial Theory
Numerical Algorithms and Complexity
Computational Learning Theory
Introduction to Cryptography
Quantum Computing
Visit the topics courses page to see which apply for this track
Topics in Graph Theory
Analysis of Algorithms II
Computational Learning Theory II
Advanced Cryptography
Theoretical Topics in Computer Science
Projects in Computer Science (advisor approval required)
Performance Analysis
Algebraic Coding Theory
Resource Allocation and Networking Games
Introduction to Probability and Statistics
Game Theoretic Models of Operation
Scheduling: Deterministic Models
Advanced Topics in Network Flows
Integer Programming
Approximation Algorithms
Optimization I & II
Stochastic Models I & II