FutureLearn Advanced Machine Learning

Overview
Discover and apply advanced statistical machine learning techniques

This online course explores advanced statistical machine learning.

You will discover where machine learning techniques are used in the data science project workflow. You will then look in detail at supervised learning statistical modeling algorithms for classification and regression problems, examining how these algorithms are related, and how models generated by them can be tuned and evaluated.

You will also look at feature engineering and how to analyse sufficiency of data.

This is an advanced course and some experience with machine learning, data science or statistical modeling is expected. Links will be provided to basic resources about assumed knowledge.

Sections of the course make use of advanced mathematics, including statistics, linear algebra, calculus and information theory. If you have prior knowledge of these areas, particularly the first two, you will obtain additional insights into the methods used. If you do not have this prior knowledge, you will still be able to achieve the learning outcomes of the course.

What topics will you cover?

• 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

Taught by
Michael Ashcroft, Sophia Knight, Lei You, and Yuan Gao