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Computer Laboratory
Teaching
Courses 2026–27
Large-scale Data Processing and Optimisation
Course materials

Department of Computer Science and Technology

Large-scale Data Processing and Optimisation

Course pages 2026–27

Computer Laboratory
Teaching
Courses 2026–27
Masters

Large-scale Data Processing and Optimisation
Advanced Graphics and Image Processing
Advanced Operating Systems
Advanced Topics in Computer Architecture
Advanced Topics in Computer Systems
Affective Artificial Intelligence
Artificial Intelligence of Things
Computer Security: Principles and Foundations
Countertechnologies: Better Technology Through Critical Practice
Digital Signal Processing
Formal Reasoning for Compilers
Information, Energy and Intelligence
Introduction to Computational Semantics and Pragmatics
Introduction to Networking and Systems Measurements
Machine Learning and the Physical World
Machine Learning for Language Processing
Machine Visual Perception
Network Architectures
Overview of Natural Language Processing
Probabilistic Machine Learning
Proof Assistants
Quantum Complexity Theory
Security, Privacy and Accountability in AI-Driven IoT Systems
Understanding Quantum Architecture
Advanced Reinforcement Learning
AI-Augmented Software Engineering
Computing for Collective Intelligence
Cybercrime
Explainable Artificial Intelligence
Homotopy Type Theory
Human Language Structures and Computation
Mobile Health
Multicore Semantics and Programming
Principles of AI-driven Neuroscience and Translational Biomedicine
Principles of Machine Learning Systems
Semantics of Concurrency
Theory of Deep Learning
Understanding Networked-Systems Performance

Large-scale Data Processing and Optimisation

Syllabus
Course materials
Assessment
Recordings

R244 2026-2027 Materials: see the course web page here!

© 2026 Department of Computer Science and Technology, University of Cambridge
Information provided by Dr Eiko Yoneki – edit page

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