Advanced Machine Learning

Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more 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. You can use the hashtag #FLMachineLearning to talk about this course on social media.

• 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