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Certificate of Achievement
has completed the following course:
The Open University and Persontyle
The Advanced Machine Learning course examines the data science workflow, the concepts and algorithms important for a variety of supervised, unsupervised, semi-supervised and reinforcement machine learning approaches, explores how these algorithms are related, and how models generated by them can be tuned and evaluated. It also examines feature engineering, missing data techniques, and sufficiency analysis of data.
4 weeks, 4 hours per week
Dave Hall
University Secretary
The Open University
Transcript
Learning outcomes
- Explain the steps of a typical data science problem, and perform those steps identified as falling under the responsibility of a machine learning specialist.
- Perform a range of pre-processing steps, including feature engineering and management of missing data, as well as explain the utility and importance of such methods.
- Apply a range of advanced machine learning techniques from all major areas of machine learning (supervised, unsupervised, semi-supervised and reinforcement learning) including tuning and regularizing these models.
- Explain how these techniques work, including the relationship between more advanced methods and the simpler methods they are built upon.
- Evaluate rigorously the performance of statistical models, and justify the selection of particular models for use.
- Evaluate rigorously the sufficiency of and suitability of data for a given modelling task
Syllabus
- Statistical Machine Learning Theory
- Analysis and Evaluation of Statistical Models
- Analysis of Data
- Supervised Learning - Artificial Neural Networks
- Supervised Learning - Kernel Methods
- Unsupervised Learning - Clustering
- Unsupervised Learning - Topic Modeling
- Feature Engineering
- Missing Data
- Basic Reinforcement Learning
- Basic Semi-Supervised Learning
Issued on 21st May 2018
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