At idealo.de we store and display millions of images. Our gallery contains pictures of all sorts. You’ll find there vacuum cleaners, bike helmets as well as hotel rooms. Working with huge volume of images brings some challenges: How to organize the galleries? What exactly is in there? Do we actually need all of it?

To tackle these problems you first need to label all the pictures. In 2018 our Data Science team completed four projects in the area of image classification. In 2019 there were many more to come. Therefore, we decided to automate this process by creating a software we called Image ATM (Automated Tagging Machine). With the help of transfer learning, Image ATM enables the user to train a Deep Learning model without knowledge or experience in the area of Machine Learning. All you need is data and spare couple of minutes!

In this talk we will discuss the state-of-art technologies available for image classification and present Image ATM in the context of these technologies. We will then give a crash course of our product where we will guide you through different ways of using it - in shell, on Jupyter Notebook and on the Cloud. We will also talk about our roadmap for Image ATM.

It is too tedious to keep on asking questions, seek explanations or set thresholds for trends or anomalies. Why not find problems before they happen, find explanations for the glitches and suggest shortest paths to fixing them? Businesses are always changing along with their competitive environment and processes. No static model can handle that. Using dynamic models that find time-delayed interactions between multiple time series, we need to make proactive forecasts of anomalous trends of risks and opportunities in operations, sales, revenue and personnel, based on multiple factors influencing each other over time. We need to know how to set what is “normal” and determine when the business processes from six months ago do not apply any more, or only applies to 35% of the cases today, while explaining the causes of risk and sources of opportunity, their relative directions and magnitude, in the context of the decision-making and transactional applications, using state-of-the-art techniques.

Real world processes and businesses keeps changing, with one moving part changing another over time. Can we capture these changing relationships? Can we use multiple variables to find risks on key interesting ones? We will take a fun journey culminating in the most recent developments in the field. What methods work well and which break? What can we use in practice?

For instance, we can show a CEO that they would miss their revenue target by over 6% for the quarter, and tell us why i.e. in what ways has their business changed over the last year. Then we provide the prioritized ordered lists of quickest, cheapest and least risky paths to help turn them over the tide, with estimates of relative costs and expected probability of success.

The field of Artificial Intelligence powered by Machine Learning and Deep Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same. New models do come into existence like Capsule Networks, but industry adoption of the same usually takes several years. Hence, in the industry, the main focus of data science or machine learning is more ‘applied’ rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.

A machine learning or deep learning model by itself consists of an algorithm which tries to learn latent patterns and relationships from data without hard-coding fixed rules. Hence, explaining how a model works to the business always poses its own set of challenges. There are some domains in the industry especially in the world of finance like insurance or banking where data scientists often end up having to use more traditional machine learning models (linear or tree-based). The reason being that model interpretability is very important for the business to explain each and every decision being taken by the model.However, this often leads to a sacrifice in performance. This is where complex models like ensembles and neural networks typically give us better and more accurate performance (since true relationships are rarely linear in nature).We, however, end up being unable to have proper interpretations for model decisions.

To address and talk about these gaps, I will take a conceptual yet hands-on approach where we will explore some of these challenges in-depth about explainable artificial intelligence (XAI) and human interpretable machine learning and even showcase with some examples using state-of-the-art model interpretation frameworks in Python!

Feature selection is one of the most important processes for pattern recognition, machine learning and data mining problems. A successful feature selection method facilitates improvement of learning model performance and interpretability as well as reduces computational cost of the classifier by dimensionality reduction of the data. Feature selection is computationally expensive and becomes intractable even for few 100 features. This is a relevant problem because text, image and next generation sequence data all are inherently high dimensional. In this talk, I will discuss about few algorithms we have developed in last 5/6 years. Firstly, we will set the context of feature selection ,with some open issues , followed by definition and taxonomy. Which will take about 20 odd minutes. Then in next 20 minutes we will discuss couple of research efforts where we have improved feature selection for textual data and proposed a graph based mechanism to view the feature interaction. After the talk, participants will be appreciate the need of feature selection, the basic principles of feature selection algorithm and finally how they can start developing their own models

The event disease happens when there is a slip in the finely orchestrated dance between physiology, environment and genes. Treatment with chemicals (natural, synthetic or combination) solved some diseases but others persisted and got propagated along the generations. Molecular basis of disease became prime center of studies to understand and to analyze root cause. Cancer also showed a way that origin of disease, detection, prognosis and treatment along with cure was not so uncomplicated process. Treatment of diseases had to be done case by case basis (no one size fits).

With the advent of next generation sequencing, high through put analysis, enhanced computing power and new aspirations with neural network to address this conundrum of complicated genetic elements (structure and function of various genes in our systems). This requires the genomic material extraction, their sequencing (automated system) and analysis to map the strings of As, Ts, Gs, and Cs which yields genomic dataset. These datasets are too large for traditional and applied statistical techniques. Consequently, the important signals are often incredibly small along with blaring technical noise. This further requires far more sophisticated analysis techniques. Artificial intelligence and deep learning gives us the power to draw clinically useful information from the genetic datasets obtained by sequencing.

Precision of these analyses have become vital and way forward for disease detection, its predisposition, empowers medical authorities to make fair and situationally decision about patient treatment strategies. This kind of genomic profiling, prediction and mode of disease management is useful to tailoring FDA approved treatment strategies based on these molecular disease drivers and patient’s molecular makeup.

Now, the present scenario encourages designing, developing, testing of medicine based on existing genetic insights and models. Deep learning models are helping to analyze and interpreting tiny genetic variations ( like SNPs – Single Nucleotide Polymorphisms) which result in unraveling of crucial cellular process like metabolism, DNA wear and tear. These models are also responsible in identifying disease like cancer risk signatures from various body fluids. They have the immense potential to revolutionize healthcare ecosystem. Clinical data collection is not streamlined and done in a haphazard manner and the requirement of data to be amenable to a uniform fetchable and possibility to be combined with genetic information would power the value, interpretation and decisive patient treatment modalities and their outcomes.

There is hugh inflow of medical data from emerging human wearable technologies, along with other health data integrated with ability to do quickly carry out complex analyses on rich genomic databases over the cloud technologies … would revitalize disease fighting capability of humans. Last but still upcoming area of application in direct to consumer genomics (success of 23andMe).

This road map promises an end-to-end system to face disease in its all forms and nature. Medical research, and its applications like gene therapies, gene editing technologies like CRISPR, molecular diagnostics and precision medicine could be revolutionized by tailoring a high-throughput computing method and its application to enhanced genomic datasets.

“Time is precious so is Time Series Analysis”

Time series analysis has been around for centuries helping us to solve from astronomical problems to business problems and advanced scientific research around us now. Time stores precious information, which most machine learning algorithms don’t deal with. But time series analysis, which is a mix of machine learning and statistics helps us to get useful insights. Time series can be applied to various fields like economy forecasting, budgetary analysis, sales forecasting, census analysis and much more. In this workshop, We will look at how to dive deep into time series data and make use of deep learning to make accurate predictions.

Structure of the workshop goes like this

• Introduction to Time series analysis
• Time Series Exploratory Data Analysis and Data manipulation with pandas
• Forecast Time series data with some classical method (AR, MA, ARMA, ARIMA, GARCH, E-GARCH)
• Introduction to Deep Learning and Time series forecasting using MLP and LSTM
• Forecasting using XGBoost
• Financial Time Series data

Libraries Used:

• Keras (with Tensorflow backend)
• matplotlib
• pandas
• statsmodels
• sklearn
• seaborn
• arch

Data is the new oil and unstructured data, especially text, images and videos contain a wealth of information. However, due to the inherent complexity in processing and analyzing this data, people often refrain from spending extra time and effort in venturing out from structured datasets to analyze these unstructured sources of data, which can be a potential gold mine. Natural Language Processing (NLP) is all about leveraging tools, techniques and algorithms to process and understand natural language based unstructured data - text, speech and so on.

Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive case- studies and hands-on examples to master state-of-the-art tools, techniques and frameworks for actually applying NLP to solve real- world problems. We leverage Python 3 and the latest and best state-of- the-art frameworks including NLTK, Gensim, SpaCy, Scikit-Learn, TextBlob, Keras and TensorFlow to showcase our examples. You will be able to learn a fair bit of machine learning as well as deep learning in the context of NLP during this bootcamp.

In our journey in this field, we have struggled with various problems, faced many challenges, and learned various lessons over time. This workshop is our way of giving back a major chunk of the knowledge we’ve gained in the world of text analytics and natural language processing, where building a fancy word cloud from a bunch of text documents is not enough anymore. You might have had questions like ‘What is the right technique to solve a problem?’, ‘How does text summarization really work?’ and ‘Which are the best frameworks to solve multi-class text categorization?’ among many other questions! Based on our prior knowledge and learnings from publishing a couple of books in this domain, this workshop should help readers avoid some of the pressing issues in NLP and learn effective strategies to master NLP.

The intent of this workshop is to make you a hero in NLP so that you can start applying NLP to solve real-world problems. We start from zero and follow a comprehensive and structured approach to make you learn all the essentials in NLP. We will be covering the following aspects during the course of this workshop with hands-on examples and projects!

• Basics of Natural Language and Python for NLP tasks
• Text Processing and Wrangling
• Text Understanding - POS, NER, Parsing
• Text Representation - BOW, Embeddings, Contextual Embeddings
• Text Similarity and Content Recommenders
• Text Clustering
• Topic Modeling
• Text Summarization
• Sentiment Analysis - Unsupervised & Supervised
• Text Classification with Machine Learning and Deep Learning
• Multi-class & Multi-Label Text Classification
• Deep Transfer Learning and it's promise
• Applying Deep Transfer Learning - Universal Sentence Encoders, ELMo and BERT for NLP tasks
• Generative Deep Learning for NLP
• Next Steps

With over 10 hands-on projects, the bootcamp will be packed with plenty of hands-on examples for you to go through, try out and practice and we will try to keep theory to a minimum considering the limited time we have and the amount of ground we want to cover. We hope at the end of this workshop you can takeaway some useful methodologies to apply for solving NLP problems in the future. We will be using Python to showcase all our examples.

Multi-Modalities

Having multiple modalities in a system gives more affordance to users and can contribute to a more robust system. Having more also allows for greater accessibility for users who work more effectively with certain modalities. Multiple modalities can be used as backup when certain forms of communication are not possible. This is especially true in the case of redundant modalities in which two or more modalities are used to communicate the same information. Certain combinations of modalities can add to the expression of a computer-human or human-computer interaction because the modalities each may be more effective at expressing one form or aspect of information than others. For example, MUST researchers are working on a personalized humanoid built and equipped with various types of input devices and sensors to allow them to receive information from humans, which are interchangeable and a standardized method of communication with the computer, affording practical adjustments to the user, providing a richer interaction depending on the context, and implementing robust system with features like; keyboard; pointing device; touchscreen; computer vision; speech recognition; motion, orientation etc.

There are six types of cooperation between modalities, and they help define how a combination or fusion of modalities work together to convey information more effectively.

• Equivalence: information is presented in multiple ways and can be interpreted as the same information
• Specialization: when a specific kind of information is always processed through the same modality
• Redundancy: multiple modalities process the same information
• Complimentarity: multiple modalities take separate information and merge it
• Transfer: a modality produces information that another modality consumes
• Concurrency: multiple modalities take in separate information that is not merged

Computer - Human Modalities

Computers utilize a wide range of technologies to communicate and send information to humans:

• Vision - computer graphics typically through a screen
• Audition - various audio outputs

Project Features

Adaptive: They MUST learn as information changes, and as goals and requirements evolve. They MUST resolve ambiguity and tolerate unpredictability. They MUST be engineered to feed on dynamic data in real time.

Interactive: They MUST interact easily with users so that those users can define their needs comfortably. They MUST interact with other processors, devices, services, as well as with people.

Iterative and Stateful: They MUST aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They MUST remember previous interactions in a process and return information that is suitable for the specific application at that point in time.

Contextual: They MUST understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulation, user profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided).

Project Demos

Multi-Modal Interaction: https://www.youtube.com/watch?v=jQ8Gq2HWxiA

Gesture Detection: https://www.youtube.com/watch?v=rDSuCnC8Ei0

Speech Recognition: https://www.youtube.com/watch?v=AewM3TsjoBk

Assignment (Hands-on Challenge for Attendees)

Real-time multi-modal access control system for authorized access to work environment - All the key concepts and individual steps will be demonstrated and explained in this workshop, and the attendees need to customize the generic code or approach for this assignment or hands-on challenge.

• Over a period of time, a lot of Knowledge bases have evolved. A knowledge base is a structured way of storing information, typically in the following form Subject, Predicate, Object
• Such Knowledge bases are an important resource for question answering and other tasks. But they often suffer from their incompleteness to resemble all the data in the world, and thereby lack of ability to reason over their discrete Entities and their unknown relationships. Here we can introduce an expressive neural tensor network that is suitable for reasoning over known relationships between two entities.
• With such a model in place, we can ask questions, the model will try to predict the missing data links within the trained model and answer the questions, related to finding similar entities, reasoning over them and predicting various relationship types between two entities, not connected in the Knowledge Graph.
• Knowledge Graph infoboxes were added to Google's search engine in May 2012

What is the knowledge graph?

▶Knowledge in graph form!

▶Captures entities, attributes, and relationships

▶More specifically, the “knowledge graph” is a database that collects millions of pieces of data about keywords people frequently search for on the World wide web and the intent behind those keywords, based on the already available content

▶In most cases, KGs is based on Semantic Web standards and have been generated by a mixture of automatic extraction from text or structured data, and manual curation work.

▶Structured Search & Exploration
e.g. Google Knowledge Graph, Amazon Product Graph

▶Graph Mining & Network Analysis
e.g. Facebook Entity Graph

▶Big Data Integration
e.g. IBM Watson

▶Diffbot, GraphIQ, Maana, ParseHub, Reactor Labs, SpazioDati

Word embeddings are the plinth stones of Natural Language Processing (NLP) applications, used to transform human language into vectors that can be understood and processed by machine learning algorithms. Pre-trained word embeddings enable transfer of prior knowledge about the human language into a new application thereby enabling rapid creation of a scalable and efficient NLP applications. Since the emergence of word2vec in 2013, the word embeddings field has seen rapid developments by leaps and bounds with each new successive word embedding outperforming the prior one.

The goal of this talk is to demonstrate the efficacy of using pre-trained word embedding to create scalable and robust NLP applications, and to explain to the audience the underlying theory of word embeddings that makes it possible. The talk will cover prominent word vector embeddings such as BERT and ELMo from the recent literature.

Cleaning, preparing , transforming, exploring data and modeling it's what we hear all the time about data science, and these steps maybe the most important ones. But that's not the only thing about data science, in this talk you will learn how the combination of Apache Spark, Optimus, the Python ecosystem and Data Operations can form a whole framework for data science that will allow you and your company to go further, and beyond common sense and intuition to solve complex business problems.

Telematics data have a potential to unlock revenue of 1.5 trillion. Unfortunately this data has not been tapped by many users.

In this case study Karthik Thirumalai would discuss how we can use telematics data to identify driver behaviour and do preventive maintenance in automobile.

There are many attempts to classify and structure the various AI techniques in the internet produced by a variety of sources with specific interests in this emerging market and the fact that some new technologies make use of multiple techniques does not make the task easier to provide an easy, top-down access and guideline through AI for business decision makers. Most sources structure the AI techniques by their core ability (e.g. supervised vs. unsupervised learning) but even this sometimes controversial (e.g. genetic algorithms). The approach taken here is to find groups of use cases that represent similar problem-solving strategies (just like distinguishing "search" from "sort" without reference to a particular technique like "Huffman search" or "qsort"). Of course, most AI techniques are combinations but with a different focus.

There are many different sorting criteria to cluster use cases and these criteria determine how well and if at all the above objectives may be achieved. The target is to find “natural” classes of problems that in an abstract way can be applied to all the corresponding use cases. Since the clustering is used to determine which AI techniques are applicable, the classes should correspond to the typical characteristics of AI technique.

{ This is a handson workshop in pgmpy package. The creator of pgmpy package Abinash Panda will do the code demonstration }

Crude oil plays an important role in the macroeconomic stability and it heavily influences the performance of the global financial markets. Unexpected fluctuations in the real price of crude oil are detrimental to the welfare of both oil-importing and oil-exporting economies.Global macro hedge-funds view forecast the price of oil as one of the key variables in generating macroeconomic projections and it also plays an important role for policy makers in predicting recessions.

Probabilistic Graphical Models can help in improving the accuracy of existing quantitative models for crude oil price prediction as it takes in to account many different macroeconomic and geopolitical variables .

Hidden Markov Models are used to detect underlying regimes of the time-series data by discretising the continuous time-series data. In this workshop we use Baum-Welch algorithm for learning the HMMs, and Viterbi Algorithm to find the sequence of hidden states (i.e. the regimes) given the observed states (i.e. monthly differences) of the time-series.

Belief Networks are used to analyse the probability of a regime in the Crude Oil given the evidence as a set of different regimes in the macroeconomic factors . Greedy Hill Climbing algorithm is used to learn the Belief Network, and the parameters are then learned using Bayesian Estimation using a K2 prior. Inference is then performed on the Belief Networks to obtain a forecast of the crude oil markets, and the forecast is tested on real data.

Incident volume reduction is one of the top priorities for any large-scale service organization along with timely resolution of incidents within the specified SLA parameters. AI and Machine learning solutions can help IT service desk manage the Incident influx as well as resolution cost by

• Identifying major topics from incident description and planning resource allocation and skill-sets accordingly
• Producing knowledge articles and resolution summary of similar incidents raised earlier

• Analyzing Root Causes of incidents and introducing processes and automation framework to predict and resolve them proactively

We will look at different approaches to combine standard document clustering algorithms such as Latent Dirichlet Allocation (LDA) and K-mean clustering on doc2vec along-with Text classification to produce easily interpret-able document clusters with semantically coherent/ text representation that helped IT operations of a large FMCG client identify key drivers/topics contributing towards incident volume and take necessary action on it.

This is a mixer workshop with lot of clinicians , medical experts , Neuroimaging experts ,Neuroscientists, data scientists and statisticians will come under one roof to bring together this revolutionary workshop.

The theme will be updated soon .

Our celebrity and distinguished presenter Srikanth Ramaswamy who is an advisor at Mysuru Consulting Group and also works Blue Brain Project at the EPFL will be delivering an expert talk in the workshop.

https://www.linkedin.com/in/ramaswamysrikanth/

{ This workshop will be a combination of panel discussions , expert talk and neuroimaging data science workshop ( applying machine learning and deep learning algorithms to Neuroimaging data sets}

{ We are currently onboarding several experts from Neuroscience domain --Neurosurgeons , Neuroscientists and Computational Neuroscientists .Details of the speakers will be released soon }

Abstract for the Neuroimaging Data Science Part of the workshop:

The study of the human brain with neuroimaging technologies is at the cusp of an exciting era of Big Data. Many data collection projects, such as the NIH-funded Human Connectome Project, have made large, high- quality datasets of human neuroimaging data freely available to researchers. These large data sets promise to provide important new insights about human brain structure and function, and to provide us the clues needed to address a variety of neurological and psychiatric disorders. However, neuroscience researchers still face substantial challenges in capitalizing on these data, because these Big Data require a different set of technical and theoretical tools than those that are required for analyzing traditional experimental data. These skills and ideas, collectively referred to as Data Science, include knowledge in computer science and software engineering, databases, machine learning and statistics, and data visualization.

The workshop covers Data analysis, statistics and data visualization and applying cutting-edge analytics to complex and multimodal neuroimaging datasets . Topics which will be covered in this workshop are statistics, associative techniques, graph theoretical analysis, causal models, nonparametric inference, and meta-analytical synthesis.

This will be a hands-on workshop how to build a custom interactive dashboard application on your local machine or on any cloud service provider. You will also learn how to deploy this application with both security and scalability in mind.

Powerful Data visualization software solutions are extremely useful when building interactive data visualization dashboards. However, these types of solutions might not provide sufficient customization options. For those scenarios, you can use open source libraries like D3.js, Chart.js, or Bokeh to create custom dashboards. While these libraries offer a lot of flexibility for building dashboards with tailored features and visualizations.

Recent advancements in AI are proving beneficial in development of applications in various spheres of healthcare sector such as microbiological analysis, discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics for translating a large-scale data into improved human healthcare. Automation in healthcare using machine learning/deep learning assists physicians to make faster, cheaper and more accurate diagnoses.

Due to increasing availability of electronic healthcare data (structured as well as unstructured data) and rapid progress of analytics techniques, a lot of research is being carried out in this area. Popular AI techniques include machine learning/deep learning for structured data and natural language processing for unstructured data. Guided by relevant clinical questions, powerful deep learning techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making.

We have successfully developed three deep learning based healthcare applications using TensorFlow and are currently working on three more healthcare related projects. In this demonstration session, first we shall briefly discuss the significance of deep learning for healthcare solutions. Next, we will demonstrate two deep learning based healthcare applications developed by us. The discussion of each application will include precise problem statement, proposed solution, data collected & used, experimental analysis and challenges encountered & overcame to achieve this success. Finally, we will briefly discuss the other applications on which we are currently working and the future scope of research in this area.

Machine learning provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Technical analysis shows in graphic form investor sentiment, both greed and fear. Technical analysis attempts to use past stock price and volume information to predict future price movements. Technical analysis of various indicators has been a time-tested strategy for seasoned traders and hedge funds, who have used these techniques to effective turn our profits in Securities Industry.

Some researchers claim that stock prices conform to the theory of random walk, which is that the future path of the price of a stock is not more predictable than random numbers. However, Stock prices do not follow random walks.

We will evaluate whether stock returns can be predicted based on historical information.

Coupled with Machine Learning, we further try to decipher the correlation between the various indicators and identify the set of indicators which appropriately predict the value

Abstract:

How to accurately detect Key Performance Indicator (KPI) anomalies is a critical issue in cellular network management. In this talk I shall introduce CNR(Cellular Network Regression) a unified performance anomaly detection framework for KPI time-series data. CNR realizes simple statistical modelling and machine-learning-based regression for anomaly detection; in particular, it specifically takes into account seasonality and trend components as well as supports automated prediction model retraining based on prior detection results. I demonstrate here how CNR detects two types of anomalies of practical interest, namely sudden drops and correlation changes, based on a large-scale real-world KPI dataset collected from a metropolitan LTE network. I explore various prediction algorithms and feature selection strategies, and provide insights into how regression analysis can make automated and accurate KPI anomaly detection viable.

Index Terms—anomaly detection, NPAR (Network Performance Analysis)

1. INTRODUCTION

The continuing advances of cellular network technologies make high-speed mobile Internet access a norm. However, cellular networks are large and complex by nature, and hence production cellular networks often suffer from performance degradations or failures due to various reasons, such as back- ground interference, power outages, malfunctions of network elements, and cable disconnections. It is thus critical for network administrators to detect and respond to performance anomalies of cellular networks in real time, so as to maintain network dependability and improve subscriber service quality. To pinpoint performance issues in cellular networks, a common practice adopted by network administrators is to monitor a diverse set of Key Performance Indicators (KPIs), which provide time-series data measurements that quantify specific performance aspects of network elements and resource usage. The main task of network administrators is to identify any KPI anomalies, which refer to unexpected patterns that occur at a single time instant or over a prolonged time period.

Today’s network diagnosis still mostly relies on domain experts to manually configure anomaly detection rules such a practice is error-prone, labour intensive, and inflexible. Recent studies propose to use (supervised) machine learning for anomaly detection in cellular networks . ellular networks, a common practice adopted by network administrators is to monitor a diverse set of Key Performance Indicators (KPIs), which provide time-series data measurements that quantify specific performance aspects of network elements and resource usage. The main task of network administrators is to identify any KPI anomalies, which refer to unexpected patterns that occur at a single time instant or over a prolonged time period.

Today’s network diagnosis still mostly relies on domain experts to manually configure anomaly detection rules such a practice is error-prone, labour intensive, and inflexible. Recent studies propose to use (supervised) machine learning for anomaly detection in cellular networks .