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CERTIFICATES

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APPLIED MACHINE LEARNING IN PYTHON

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.
This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

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BAYESIAN METHODS FOR MACHINE LEARNING

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine.
When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money. In this online HSE course we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases can be found with Bayesian methods.

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INTRODUCTION TO COMPUTATIONAL STATISTICS FOR DATA SCIENTISTS

The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST). What it does cover is:
The basics of Bayesian statistics and probability
Understanding Bayesian inference and how it works
The bare-minimum set of tools and a body of knowledge required to perform Bayesian inference in Python, i.e. the PyData stack of NumPy, Pandas, Scipy, Matplotlib, Seaborn and Plot.ly
A scalable Python-based framework for performing Bayesian inference, i.e. PyMC3
With this goal in mind, the content is divided into the following three main sections (courses).
Introduction to Bayesian Statistics - The attendees will start off by learning the the basics of probability, Bayesian modeling and inference in Course 1.
Introduction to Monte Carlo Methods - This will be followed by a series of lectures on how to perform inference approximately when exact calculations are not viable in Course 2.
PyMC3 for Bayesian Modeling and Inference - PyMC3 will be introduced along with its application to some real world scenarios.
The lectures will be delivered through Jupyter notebooks and the attendees are expected to interact with the notebooks.

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SMART MATERIALS: MICROSCALE AND MACROSCALE APPROACHES

Smart materials represent a cutting-edge global trend both in fundamental science and emerging technologies. Smart materials science is a truly interdisciplinary area at the intersection of physics, chemistry, chemical engineering, mathematical simulations, nanotechnology, biotechnology, and etc. An online course in smart materials will be of obvious interest for everyone who is interested in modern materials science and emerging trends in engineering, biotechnology, and medicine.
A broad variety of materials are actually considered as smart ones: from shape memory alloys to polymer nanosystems. We expect that the Coursera audience will benefit from a compact and simple online course that integrates specific modern aspects and trends of fabrication, modification, characterization, and applications of smart materials. With this course, we would like to introduce specific aspects of an exciting interdisciplinary area “Smart Materials” in a way it can be easily understood by a broad audience. We will focus on how remarkable properties of smart materials correlate with simple structural features at nanoscale and microscale, discuss various methods to characterize materials with smart properties. We will highlight inspiring trends in applications of smart materials. For a microscale approach, we offer the Lab on a Chip technology and tell a story on to use microfluidic chips for fabricating smart systems. For a macroscale approach, we introduce modern analytical methods that are used to characterize smart materials We expect that this course will be interesting for a broad audience that is keen to learn more about how smart materials contribute to well-established and emerging technologies.

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RECENT ADVANCES IN FREEFORM ELECTRONICS

In this course, the learners will understand fundamental basic backgrounds of p-n junctions, diodes and MOSFETs (Metal Oxide Semiconductor Field Effect Transistors). Also, recent approaches on flexible / stretchable electronics, transparent optoelectronics, and printed electronics using one dimensional or two dimensional nanomaterials will be introduced. Furthermore, the formation of high-performance, transparent thin films or conductors using novel materials such as cellulose nanofibers or metal nanofibers is included for course materials. Examples of device fabrications, including touch screen panels, gas / chemical / bio sensors, smart contact lenses, wireless and transparent antennas, and OLEDs, are also introduced for next generation electronic devices.

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INFLUENCING: STORYTELLING, CHANGE MANAGEMENT AND GOVERNANCE

The ability to influence without force or coercion is important in any organization and at all levels. Modern managers must master the art of persuasive communication, adapt to the needs of the audience, construct a good narrative and inspire the audience to act (Communicate with Impact). They must learn to design and negotiate both informal and complex agreements, and do this in diverse workplace cultures (Negotiate and Resolve Conflict).
The Specialization applies influencing methods in two areas of great importance: change management and risk governance. Successful change management eludes many individuals and organizations, so we propose a new approach to enable you to inspire and lead transformations (Manage Change). Similarly, in the post-financial crisis environment, many organizations acknowledge the need for effective risk governance but few, if any, have achieved it. We present evidence-based methods that will help your organization achieve its objectives (Engage the Board).

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DESIGNING LEARNING INNOVATION

Where to start to innovate your teaching? But before that, what does it mean to innovate in the classroom? Designing Learning Innovation aims to put the designing culture at the service of learning innovation, supporting those who do not have a specific pedagogical background and those who wish to learn the basic tools of a good teaching design then to continue exploring the frontiers of innovation.
A set of logical and methodological tools to innovate teaching, finding the most suitable approaches with one’s own vision of the teaching-learning experience. INTENDED LEARNING OUTCOMES (ILOs) If you actively participate in this course, at the end you will be able to: Use basic theoretical-methodological tools (such as Constructive Alignment and the Learning Innovation Network) to enhance teaching coherence between learning objectives, assessment methods and learning experiences; apply pedagogical frameworks of the active learning methodology to the design either an individual teaching module or an entire teaching path; evaluate advantages and disadvantages, in the field of your own teachings, of traditional evaluation approaches compared to the new assessment strategies, in particular those oriented to the formative evaluation; apply simple strategies for managing the active class in small, medium and large classrooms; designing and producing quality teaching materials also enhancing the availability of “Open Educational Resources”. THE MOOC-BOOK: MATERIALS AND ACTIVITIES The MOOC is realized in a strongly integrated way with the book “Designing Learning Innovation” published by Pearson (the english version is the translation of the italian one already published and accessible here https://www.ibs.it/designing-learning-innovation-ebook-inglese-susanna-sancassani/e/9788891926067) In the course you will find video lessons and infographic articulated as in the book. You will also encounter different types of activities which will contribute to make your experience richer and more complete.

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STEM AMBASSADOR 

ISSUED BY STEM LEARNING

Inspire young people to engage more enthusiastically with STEM subjects, continue to study them and explore STEM careers.
Activities: career talks, practical activities, speed networking, mentoring, visits to workplaces, science fairs, becoming a school governor and supporting a STEM Club.

STEM Ambassadors are volunteers from a wide range of science, technology, engineering and mathematics (STEM) related jobs and disciplines across the UK. We appreciate the invaluable support they provide to teachers, community leaders and others working with young people. STEM Ambassador Digital Badges recognise volunteers who have made a positive impact by displaying remarkable commitment and enthusiasm to inspiring the next generation with STEM.

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PGR REPRESENTATIVE | UNIVERSITY OF MANCHESTER

PGR student reps provide a voice for fellow students in their Division or cohort by ensuring their concerns are raised during University decision-making processes.

The Doctoral Academy's PGR rep scheme is a valued part of the PGR community in the Faculty of Biology, Medicine and Health. We believe that PGR reps help get PGR voices heard while building a welcoming postgraduate researcher environment.

Reps' responsibilities include:

  • attending School rep forums to feed back fellow students' concerns and discuss issues and areas of good practice;

  • disseminate useful information to students;

  • liaise with fellow reps, PGRs and the local divisional Senior PGR Tutor to arrange activities, such as seminars, socials or induction events (optional);

  • School reps will attend the Doctoral Academy Leadership Team monthly meetings, the School Postrgaduate Comittee meetings and the Manchester Doctoral College monthly meetings, on a rota basis.

PGR rep activities include attending careers seminar series, coffee mornings, sports days and International Women's Day initiatives.

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