22 Apr Free Online Course on Advanced Machine Learning
|The Open University||Learning Platform: FutureLearn|
|Start Date: 23rd April 2018||Advanced|
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.
What will you learn in this course?
By the end of the course, you’ll be able to…
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
Who is this course for?
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.
Who are the instructors?
Mike Ashcroft is Chief AI Officer with Persontyle, researches and teaches at Uppsala University, Sweden, and has founded two companies specializing in AI/ML consultancy and project management.
Sophia Knight is a postdoc at Uppsala University. She works on analyzing knowledge in dynamic multi-agent systems.
PhD student in Computer Science at Uppsala University. His research interests focus on optimization and machine learning in the domain of mobile communications and networking.
Alex is interested in machine learning, as applied to (social) robotics. In particular, he is interested in deep/reinforcement/neuro-based learning approaches to robotic perception and control
Cost of course:
FREE but with limited access to the course for 6 weeks. To upgrade to unlimited access to the course costs about 62£ as at April 2019
4 weeks, 4 hours per week
How to enrol:
Click here to enroll