Machine Learning Techniques for Advanced Data Analysis

After the course you will:

• Be able to set up a basic Machine Learning Analysis from beginning to end: From retrieving, cleaning, and curating the data, to establishing the information level, extracting patterns, and finding outliers.
• Be acquainted with a number of simple and advanced tools for data cleaning, statistical analysis of very large datasets, data stream analysis, finding patterns and outliers in Big Data, and collecting data from instruments and devices (e.g. Internet of Things (IoT)).

Throughout the course, we will use examples of structured datasets in a commercial context, which will be used to demonstrate the different steps in Machine Learning Analysis. Participants will have the chance to ask questions about specific data and challenges and develop ML models on their own data cases in collaboration with our experts from The Niels Bohr Instute and Berkeley Lab.

Core elements

• Data cleaning and statistical methods: Detecting and correcting (or removing) corrupt or inaccurate records, and robust statistical methods along with cross checks for data with very large variations.
• General introduction to Machine Learning algorithms: Basic philosophy of ML, a variety of methods, how they work behind the scenes, their strengths and weaknesses, and their applications.
• Selection of machine learning algorithms: Specific algorithms that works well, spans from simple to advanced, and covers a wide range of problems. Boosted decision trees, random forests, deep neural networks, convolutional networks, and large-scale exact nearest neighbour search.
• Finding patterns and outliers in Big Data: Which methods can be used to identify sparse patterns in very large datasets, and how can we identify data that does not follow the general pattern of a dataset?.
• Collecting data from instruments and devices: How to collect, store, and analyze data from a multitude of sources.

Tools/methods introduced

• Software for ML Analysis: Common software frameworks for running ML algorithms efficiently (SciKit Learn, Keras, PyTorch, etc.).
• Methods for interpreting your ML output, for understanding the reasons behind performance, and techniques for mitigating biases in algorithm decisions
• Data curation: How to select data for long time curation, along with systems, techniques, and model results

We will primarily be working with Python; however, all techniques that are covered are easily implemented with all standard data-analysis languages. 

A teaching assistant will be present to help with the technical parts.

• Data Scientists and Analysts: Professionals who work (or have worked) with data and want to enhance their skills with machine learning techniques.
• IT Professionals: Those in software development, system architecture, or database management looking to expand their skill set into ML.
• Industry Specialists: Professionals from sectors such as healthcare, finance, retail, and logistics, where big data analysis and ML applications can provide competitive advantages.
• Product Managers and Developers: Individuals looking to incorporate ML features into their products or services.
• Research and Development Personnel: Those working in R&D who can leverage ML for innovative solutions and advancements in their fields.
• Government Officials: In roles related to data governance, urban planning, and public services who can utilize ML for improved decision-making and operational efficiency.

The course is focused on Machine Learning and Big Data Analysis, so a prerequisite is that you have some background in statistics and/or conventional data analysis. This course assumes you have studied to at least a Bachelor degree or have several years of data analysis experience.