MSE as Maximum Likelihood

Jessica YungMachine LearningLeave a Comment

MSE is a commonly used error metric. But is it principly justified? In this post we show that minimising the mean-squared error (MSE) is not just something vaguely intuitive, but emerges from maximising the likelihood on a linear Gaussian model. Defining the terms Linear Gaussian Model Assume the data is described by the linear model , where . Assume is … Read More

Maximum Likelihood as minimising KL Divergence

Jessica YungMachine LearningLeave a Comment

Sometimes you come across connections that are simple and beautiful. Here’s one of them! What the terms mean Maximum likelihood is a common approach to estimating parameters of a model. An example of model parameters could be the coefficients in a linear regression model , where is Gaussian noise (i.e. it’s random). Here we choose parameter values that maximise the … Read More

RNNs as State-space Systems

Jessica YungEngineering, Machine LearningLeave a Comment

It’s fantastic how you can often use concepts from one field to investigate ideas in another area and improve your understanding of both areas. That’s one of the things I enjoy most. We’ve just started studying state-space models in 3F2 Systems and Control (a third-year Engineering course at Cambridge). It’s reminded me strongly of recurrent neural networks (RNNs). Look at … Read More

Effective Deep Learning Resources: A Shortlist

Jessica YungArtificial Intelligence, Data Science, Education, Machine Learning, StudyingLeave a Comment

A lot of people ask me how to get started with deep learning. In this post I’ve listed a few resources I recommend for getting started. I’ve only chosen a few because I’ve found precise recommendations to be more helpful. Let me know if you have any comments or suggestions! Prelude: If you’re new to machine learning Deep learning is … Read More

AlphaGo Zero: An overview of the algorithm

Jessica YungArtificial Intelligence, HighlightsLeave a Comment

In this post I go through the algorithms presented in the groundbreaking AlphaGo Zero paper using pseudocode. The objective is to provide a high-level idea of what the model does. Why AlphaGo Zero matters Last week, Google DeepMind published their final iteration of AlphaGo, AlphaGo Zero. To say its performance is remarkable is an understatement. AlphaGo Zero made two breakthroughs: … Read More

How I completed Udacity’s Machine Learning ND in just over one month

Jessica YungCareers, Education, Machine LearningLeave a Comment

How can we learn more effectively in a short amount of time? In this post, I describe how I went about finishing Udacity’s Machine Learning Nanodegree in about a month when it usually takes 6-12 months. I hope this will give you some insight and ideas as to how you might work more effectively to accomplish your own learning goals. Sections in … Read More