Have you wondered about the design strategies behind temperature controllers, quad-copters, or self-balancing scooters? Are you interested in robotics, and have heard of, or tried, “line-following" or “PID control” and want to understand more? Feedback control is a remarkably pervasive engineering principle. Feedback control uses sensor data (e.g. brightness, temperature, or velocity) to adjust or correct actuation (e.g. steering angle, motor acceleration, or heater output), and you use it all the time, like when you steer a bicycle, catch a ball, or stand upright. But even though applications of feedback are very common, the subject is an uncommonly compelling example of mathematical theory guiding practical design. In this engineering course we will introduce you to the theory and practice of feedback control and provide a glimpse into this rich and beautiful subject. Each week we will begin with a mathematical description of a fundamental feedback concept, combined with on-line exercises to test your understanding, and will finish with you designing, implementing, measuring, and analyzing a hardware system, that you build, for controlling a propeller-levitated-arm feedback system. You will not need a background in calculus or software engineering to succeed in this class but you should be familiar with algebra and mechanical forces, have some exposure to complex numbers, and be comfortable with modifying mathematical formulas in short computer programs. This is a lab course, and in order to complete the weekly assignments, you will need to purchase/acquire a list of parts. To make sure you receive your parts before the class begins, you should register promptly, so that you can access the lists of parts and international vendors.

Do you like biology, biotechnology, or genetic engineering? Are you interested in computer science, engineering, or design? Synthetic Biology is an innovative field bringing together these subject areas and many more to create useful tools to solve everyday problems. This introductory synthetic biology course starts with a brief overview of the field and then delves into more challenging yet exciting concepts. You will learn how to design your very own biological regulatory circuits and consider ways in which you can apply these circuits to real-world problems we face today. From basic oscillators, toggle switches, and band-pass filters to more sophisticated circuits that build upon these devices, you will learn what synthetic biologists of today are currently constructing and how these circuits can be used in interesting and novel ways. Join us as we explore the field of synthetic biology: its past, present, and promising future!

In this course, part of the Algorithms and Data Structures MicroMasters program, you will learn how graph algorithms are used in two fundamental problems in modern biology: How do we sequence a genome? How do we construct an evolutionary “Tree of Life?" In the first part of the course, you will learn how genome sequencing relies on using a graph to assemble millions of tiny DNA fragments into a contiguous genome. We will then shift gears and learn how to construct an evolutionary tree of life from genome data.

Gene sequences and the rest of the genome play an important role in determining how an organism functions normally and reacts when situations change. DNA sequences can also be used to determine relationships between organisms and form the underpinnings of the Tree of Life. Since DNA sequences play such an important role in any organism it should not be surprising that any changes to a sequence could lead to alterations in behavior or response. For example, a small number of specific changes in DNA sequence have been shown to lead to tumor development in mammals or the production of enzymes with altered properties. One of the jobs of a bioinformatician is to help determine where these changes are in a DNA sequence and sort out in that context what effects may result, which is usually done by aligning the sequences in question. In this course, part of the Bioinformatics MicroMasters program, you will learn about the theory and algorithms behind DNA alignments, practice doing alignments manually, and then perform more complicated alignments using web and software based approaches. This course is part of the Bioinformatics MicroMaster’s program from UMGC. Upon completion of the program and receipt of the verified MicroMaster’s certificate, learners may then transition into the full UMGC Master’s Program in Biotechnology with a specialization in Bioinformatics. See the MicroMasters program page for more information.

If you look at two genes that serve the same purpose in two different species, how can you rigorously compare these genes in order to see how they have evolved away from each other? In the first part of the course, part of the Algorithms and Data Structures MicroMasters program, we will see how the dynamic programming paradigm can be used to solve a variety of different questions related to pairwise and multiple string comparison in order to discover evolutionary histories. In the second part of the course, we will see how a powerful machine learning approach, using a Hidden Markov Model, can dig deeper and find relationships between less obviously related sequences, such as areas of the rapidly mutating HIV genome.

Building a fully-fledged algorithm to assemble genomes from DNA fragments on a real dataset is an enormous challenge with major demand in the multi-billion dollar biotech industry. In this capstone project, we will take the training wheels off and let you design your own optimized software program for genome sequencing. This big data challenge will cover the entire MicroMasters program. After a brief introduction to the steps required to build a genome assembler, we will let you take steps on your own to start working with real data taken from a sequencing machine and see if you can design genome assembly software that can compete with popular software used in hundreds of sequencing labs around the world every day.

How much would you like your smart home to know about you? Has your data been harvested and used for political advertising on social media? Would you be happy to be profiled by a predictive policing AI? As we create more data-driven technologies, those issues become increasingly urgent. We must begin to ask not only ‘what can we do?’, but also ‘what should we do?’ How should we design new technologies to make sure they are used for good, not bad purposes? The ‘good’, the ‘bad’, and the ‘should’ are a domain of ethics, and a basis for other important concepts such as justice, fairness, rights, respect. They further inform the law and what is legal. Finally, they are at the roots of an extremely important currency in the modern economy: trust. This story-driven course is taught by the leading experts in data science, AI, information law, science and technology studies, and responsible research and innovation, and informed by case studies supplied by the digital business frontrunners and tech companies. We will look at real-world controversies and ethical challenges to introduce and critically discuss the social, political, legal and ethical issues surrounding data-driven innovation, including those posed by big data, AI systems, and machine learning systems. We will drill down into case studies, structured around core concerns being raised by society, governments and industry, such as bias, fairness, rights, data re-use, data protection and data privacy, discrimination, transparency and accountability. Throughout the course, we will emphasise the importance of being mindful of the realities and complexities of making ethical decisions in a landscape of competing interests. We will engage with data-based contexts such as facial recognition, predictive policing, medical screening, smart homes and cities, banking, and AI, to explore their social implications and the tools required to minimise harm, promote fairness, and safeguard and increase human autonomy and well-being. We address cutting edge issues being grappled with by practitioners and new approaches emerging in industry and offer the opportunity for participants to develop and feedback solutions. Completing this course will help you understand the challenges we are facing, and inspire you to design, criticise, and develop better intelligent systems to shape our future.

As the Internet of Things (IoT) continues to grow so will the number of privacy and security concerns and issues. As a professional working in the field, it is essential to understand the potential security risks and how to best mitigate them. In this course, you will learn about security and privacy issues in IoT environments. We’ll explore the organizational risks posed by IoT networks, and the principles of IoT device vulnerabilities. We’ll also look at software and hardware IoT Applications for industry. With billions of devices tracking our every move, privacy is a critical issue. We will explore and discuss the social and commercial implications the IoT brings to society.

Want to produce and record your own music? This course will help you do that by showing you how to apply new technologies to your own creative practice, using freeware and browser based apps. Music Technology Foundations draws on Adelaide’s world-class pioneering expertise in making electronic music, to provide a great foundation to a career in music and to enable any learner to use technology in creative ways. In this course, you’ll learn about the core principles of music technology, including sound, audio, MIDI, effects and sequencing.

Improvements in modern biology have led to a rapid increase in sensitivity and measurability in experiments and have reached the point where it is often impossible for a scientist alone to sort through the large volume of data that is collected from just one experiment. For example, individual data points collected from one gene expression study can easily number in the hundreds of thousands. These types of data sets are often referred to as ‘biological big data’ and require bioinformaticians to use statistical tools to gain meaningful information from them. In this course, part of the Bioinformatics MicroMasters program, you will learn about the R language and environment and how to use it to perform statistical analyses on biological big datasets. This course is part of the Bioinformatics MicroMaster’s program from UMGC. Upon completion of the program and receipt of the verified MicroMaster’s certificate, learners may then transition into the full UMGC Master’s Program in Biotechnology with a specialization in Bioinformatics without any application process or testing. See the MicroMasters program page for more.