Technical University of Denmark


Large data analytics

About the course:

In modern manufacturing, with a strong push towards digitalization, vast amounts of production data are now being generated, collected and stored. This data contains information that can be used for process understanding, improvement, optimisation and control.

The course provides you with tools that can help and accelerate the extraction of such valuable information from available data.

The course provides:

  • An overview of a range of frequently used data analytics methods for process understanding, improvement and surveillance. A mixture of classical and relatively new methods in large data analytics will be covered.

  • Immediate possibility to the participants to apply the methods covered during the course.

  • Working knowledge through hands-¬on exercises for which the participants will work on provided data or on their own data.

  • A brief overview of functionalities in The statistical software R that will be used for live demos and exercises throughout the course (R which is free statistical software that can be downloaded from https://cran.r-¬project.org .)

Who is the course for?

The course is aimed at all people who deal with data in their daily work. The participants are expected to have an introductory level knowledge in statistics.


artificial intelligence

About the course:

Artificial Intelligence (AI) is the science and engineering of making intelligent machines, in particular intelligent computer programs.

The area is much harder to define and delimit than most other areas of science and engineering, since intelligence is many different things, e.g. social, linguistic or emotional intelligence, and at very different levels. Correspondingly, AI is many very distinct techniques to mimic different aspects of human intelligence, and at very different levels. This makes it difficult to navigate in the area of AI and understand exactly what it can and cannot do.

One of the goals of the course is to clarify the different paradigms, methods and subareas of AI, to provide a clear picture of what AI is, to make it easier to understand the current developments in the area, and to understand the possibilities and limitations of AI methods in general.

The course will address both the benefits and the potential negative consequences, including discussions of the legal, ethical and philosophical aspects of AI.

This course provides insight into:

  • In depth knowledge about contemporary applications of AI from technical, legal, ethical and economical perspectives

  • The main paradigms of AI and explain their respective strengths and weaknesses

  • The most prevalent methods in current AI and their respective application areas, strengths and weaknesses

  • Which methods of AI could potentially solve a given human task

  • Main trade-offs in designing AI systems in terms of generality vs. scalability, flexibility/learnability vs. predictability/explainability, etc.

  • Impact of increase in available data and computing power on AI

  • characteristic differences between human and machine intelligence, and the potential in human-machine collaboration

Course content:

Half of the course will be dedicated to technical aspects of AI, introducing the main paradigms, methods and application areas. The other half will be discussions of legal, societal, economical, ethical, and philosophical aspects.


Tools for
data science

About the course:

Software tools for efficiently analyzing, indexing, searching, and processing massive data sets is rapidly evolving, leading to new and exciting opportunities for easily building advanced data science applications.

This course covers key state-of-the art tools for building advanced data science applications and provides a hand-on experience with these tools.

The course provides:

  • An overview of key software tools for data science applications, including tools for massive parallel computation, filtering, streaming, advanced data bases, clustering, and indexing.

  • Hand-on experience with state-of-the-art software tools through guided exercises that cover basic use each tools and building small example applications.

  • A basic understanding of the underlying technology used in the tools.

Who is the course for?

Developers or managers working with data science applications. Preferably with a basic programming experience in Python.


Machine learninG

About the course:

Large, successful organisations or companies like Google and Amazon have invested huge amounts in machine learning, which they use to analyze their customers' interests and behaviors to optimize their products, processes and marketing.

In this course, you will learn to master the essential machine learning models in Matlab, Python, or R.

The course builds on the successful DTU course and textbook: "Introduction to Machine Learning and Data Mining", and is taught by DTU Associate Professors Morten Mørup and Mikkel N. Schmidt who have 10+ years of experience in machine learning research.

The course provides:

  • A strong intuition of different machine learning algorithms and knowledge of which methods you can apply on a given problem.

  • Skills in specific topics like model construction (feature extraction, dimensionality reduction, cross-validation, and model selection); supervised learning (linear regression, logistic classification, decision trees, artificial neural networks, and ensemble learning); and unsupervised learning (hierarchical clustering, kernel density estimation, mixture modeling, association mining, and outlier detection).

  • In particular, the course will establish the major steps in any machine learning pipeline from preparing the data, to modeling the data and disseminating the results, and throughout the course we encourage participants to apply the methods learned on their own data and problem domains.

Who is the course for?

The target audience is anyone who is interested in machine learning, who is comfortable with 1st year university math (linear algebra and basic probability) and ready to endeavor into creating and programming machine learning algorithms.

Prerequisites:

  • Linear algebra as covered in this study material

  • Basic probability theory study material

  • Basic programming skills in either Matlab, Python, or R


Using R in
data science

About the course:

With the digital revolution, software tools is becoming ever more important for Data Science.

R is a free object-oriented software environment for statistical computing and graphics, and has become the tool of choice in many environments over recent years, when it comes to data science.

R has advanced levels of flexibility in statistical computing that promotes the design of analysis exactly as you want it, and state of the art graphics capabilities.

This course takes the participants all the way from the very basics to advanced data handling and graphics in three intensive days.

The course provides:

  • A natural progressive comprehension of the R environment, where the knowledge is built up from simple calculator applications to interactions with other software and construction of semi-advanced graphics.

  • A guided tour to setting up your own data analysis in R, take data in and take results out.

  • Hand-on exercises with practical issues in data science, letting you familiarize yourself with the programming environment.

  • A platform to explore uses of R within you own field.

Who is this course for?

This course is designed for people with an interest in data analysis, both those with little or no experience in software tools for data science, and those who wish to investigate a new or alternative tool for their established expertise in other languages, like t. ex. SAS.

Basic programming experience is not needed, but will of course easy the learning. An interest for applying analytical software is essential.


Deep learning

About the deep learning course:

Machine perception of natural signals has improved a lot in the recent years thanks to deep learning (DL). Improved vision systems with DL will make self-driving cars possible and is leading to more accurate image-based medical diagnosis.

Improved speech recognition and natural language processing with DL will lead to many new intelligent applications within health-care and IT.

Pattern recognition with DL in large datasets will give new tools for drug discovery, condition monitoring and many other data-driven applications. Applications in other areas such as natural language processing, biology, finance and robotics are numerous.

Deep learning is an important tool for the leading IT companies' ambition about becoming machine learning and AI first companies. This course is designed to help you integrate deep learning into your organisation.

The deep learning course provides:

  • Knowledge about the latest developments in the field.

  • Opportunities and pitfalls.

  • Company access to computational frameworks making it possible to apply learnings directly to your company context.

  • Insight into well-established methods like feed-forward, convolutional and recurrent neural networks.

  • Insight into frontier technology like un-, semi- and reinforcement learning that can be expected to play a larger role in the coming years.

Who is the deep learning course for:

The course is aimed at all who have a professional interested in deep learning and who have knowledge of mathematics, which is in line with the mathematics that will be used, as first year students at DTU. (Specifically linear algebra and probability theory).

Prerequisites:

Programming preferably in Python, basic probability theory and basic linear algebra. Please bring your own laptop.

Computer frameworks

PyTorch og AWS GPU computing.


Certificate in entrepreneurial leadership

To stay ahead in an increasingly competitive market space, established companies need to be able to renew their focus and find new business opportunities while, at the same time, optimizing their core business.

As a CEL™ participant you will learn how to clarify ideas, how to incubate and mature these ideas into businesses, and ultimately how to accelerate them to enhance company growth. You will also learn how to navigate new business projects in an organization with a highly optimized core business.

CEL™ is action learning based, meaning that you will apply your gained knowledge to a specific project in your company. This allows you to create direct value for your company throughout the programme. Class structure is team based with a maximum of 10 company teams, of 2-3 participants each, working on a new business creation project for their company.

The core concepts of the programme are based on the findings of cutting edge research from USA and Europe on how companies renew their core business. The programme draws upon highly experienced international experts from the world’s leading business schools who both teach and coach participants in how to apply what they learn to their specific projects.


Innovation
Health Challenge

Focused on establishing and supporting innovation activities within healthcare and life science this program deals with innovation leadership challenges as experienced by top management in initiating, supporting and operating innovation activities.

Top 5 Key Elements

  1. Experience Innovation and Leadership amongst Healthcare professionals at UC Berkeley, California. 

  2. Identifying, dealing and implementing specific leadership challenges

  3. A plan of actions for implementation

  4. An active corps of “innovation champions” among hospital & healthcare executives

  5. Lifelong experience with unique access to a topleadership network among Healthcare professionals