MS Data Science Additional Module Details
Modules Taken & Topics Covered:
Bayesian Inference:
Posterior/Prior/Conditional/Marginal/Predictive Distributions, Gibbs Sampler, Changepoint Models, Monte Carlo Markov Chains, Metropolis-Hastings Algorithm (Independence Sampler and Random-Walk Metropolis)
Likelihood Inference:
Single and Multi-parameter (log) Likelihoods, Hypothesis Testing, Confidence Intervals (Normality and Deviance-based), Point Processes
Distributed Artificial Intelligence:
Single and Multi-Agent Reinforcement Learning, MDPs and POMDPs, Game Theory, Ensemble Systems
Applied Data Mining (NLP):
Data Scraping, Tokenization, Annotation, Tf-idf
Generalized Linear Models:
Linear Algebra within Linear Regression, Model Checking Diagnostics, GLMs and Link Functions, Distributions within Exponential Family, Deviance
Data Mining (Neural Networks):
Application and theory of Neural Networks
Posterior/Prior/Conditional/Marginal/Predictive Distributions, Gibbs Sampler, Changepoint Models, Monte Carlo Markov Chains, Metropolis-Hastings Algorithm (Independence Sampler and Random-Walk Metropolis)
Likelihood Inference:
Single and Multi-parameter (log) Likelihoods, Hypothesis Testing, Confidence Intervals (Normality and Deviance-based), Point Processes
Distributed Artificial Intelligence:
Single and Multi-Agent Reinforcement Learning, MDPs and POMDPs, Game Theory, Ensemble Systems
Applied Data Mining (NLP):
Data Scraping, Tokenization, Annotation, Tf-idf
Generalized Linear Models:
Linear Algebra within Linear Regression, Model Checking Diagnostics, GLMs and Link Functions, Distributions within Exponential Family, Deviance
Data Mining (Neural Networks):
Application and theory of Neural Networks
- Location: Lancaster, United Kingdom
- Start Date: October 2017
- Graduation Date: December 2018
- Awards: Graduated with Distinction (Highest Academic Honors)