Athens Tech College

Deep Learning

Deep learning is a hot topic that has found multiple areas of application in the industry and business. Deep learning is the extension of Neural Networks (NN) that includes some new developments in training algorithms and uses the versatility of the computing power and data storage of the cloud. The module briefly introduces neural networks, explains how they work, how they are trained, and how they are deployed. Furthermore, it discusses the recent developments in training algorithms, NN structures, and cloud deployment, to conclude with the practical application of Artificial Intelligence solutions that we now call Deep Learning.

Continuous and Agile Software Engineering

The aim of this unit is to present contemporary topics in Software Engineering. It starts with a brief overview of software engineering principles and then focuses on modern approaches to software development and management, software architecture, and advanced techniques as formal methods. Students will actively contribute to the lectures, by reviewing and presenting material on contemporary software engineering topics.

Industrial Group Project

The purpose of this unit is to provide students with the opportunity to integrate and apply the skills and the knowledge they have acquired so far in their studies to a realistic problem. Students are exposed to the processes involved in the team-based development of software through real projects that are provided by companies from the software industry.

Research Skills and Dissertation Preparation

This unit intends to introduce students to the research topics and techniques that are commonly employed in software engineering and telecommunication science. Students will be exposed to the principles of report writing, literature reviewing, and research designs and approaches. These research approaches will include the design of data collection and analysis methods, as well as representation and interpretation of the results. An introduction to tools facilitating quantitative and qualitative analysis will also be provided. The unit is enriched with a number of exercises, case studies and discussions and concludes with practical guidelines of how to write a successful dissertation.

Business Intelligence

Business intelligence (BI) systems are applications and technologies for gathering, storing, analyzing, and accessing information for better business decision-making. The application areas of such systems include measuring and monitoring key performance indicators, benchmarking and forecasting sales, performing data mining and analysis of customer information.
This course provides a comprehensive introduction to the concepts, components, techniques and applications of BI and related areas. Students will gain awareness, in a gradual manner, of the complete life cycle of implementing and managing BI systems.

Big Data Engineering

This unit explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. Main aspects and challenges of Big Data will be addressed by introducing relevant algorithms, among others, in the areas of MapReduce, data stream mining and recommendation systems.
Additionally, this course provides a detailed description and hands-on experience to the cutting-edge open-source software Apache Hadoop and Apache Kafka.
Students will be introduced and gain awareness, in a granual manner, to the concepts, algorithms and techniques that cover key Big Data topics.

Data Mining and Machine Learning

This unit addresses the contemporary aspects, algorithms and challenges of mining big data by introducing data mining and machine learning algorithms. The unit aims to provide students with the necessary skills, knowledge and hands-on experience regarding data exploration, data preprocessing, visualization, unsupervised machine learning algorithms, clustering and other contemporary topics like dimensionality reduction, anomaly detection, pattern recognition, association rules and recommendation systems.

Advanced Artificial Intelligence Techniques

This unit starts with an in-depth introduction to Artificial Intelligence problem solving techniques and covers contemporary and advanced AI algorithms and application area. Main aspects and challenges that will be addressed by introducing relevant theory and hands-on practical sessions include search algorithms (brute-force, heuristic), supervised Learning Algorithms, Neural Networks, Deep Learning, Natural Language Processing and image classification.

Cloud Engineering

The unit aims at introducing students to a range of foundational –as well as more advanced– topics pertaining to cloud engineering, including:

• Fundamental concepts: foundations of cloud computing, principles of cloud computing, benefits, cloud service models, cloud deployment models, virtualisation technologies.
• Cloud platforms: AWS EC2, AWS Lambda, Heroku.
• Cloud-native applications: SOA, Microservices, Docker, Kubernetes, DevOps, challenges involved.
• Quality of provisioned services: scaling, IoT, Fog Computing, Mobile Edge Computing, SLAs.
• Security issues: access control, colocation attacks, side-channel attacks.

Dissertation

Year 1

Deep Learning

Deep learning is a hot topic that has found multiple areas of application in the industry and business. Deep learning is the extension of Neural Networks (NN) that includes some new developments in training algorithms and uses the versatility of the computing power and data storage of the cloud. The module briefly introduces neural networks, explains how they work, how they are trained, and how they are deployed. Furthermore, it discusses the recent developments in training algorithms, NN structures, and cloud deployment, to conclude with the practical application of Artificial Intelligence solutions that we now call Deep Learning.

Big Data Engineering

This unit explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. Main aspects and challenges of Big Data will be addressed by introducing relevant algorithms, among others, in the areas of MapReduce, data stream mining and recommendation systems.
Additionally, this course provides a detailed description and hands-on experience to the cutting-edge open-source software Apache Hadoop and Apache Kafka.
Students will be introduced and gain awareness, in a granual manner, to the concepts, algorithms and techniques that cover key Big Data topics.

Continuous and Agile Software Engineering

The aim of this unit is to present contemporary topics in Software Engineering. It starts with a brief overview of software engineering principles and then focuses on modern approaches to software development and management, software architecture, and advanced techniques as formal methods. Students will actively contribute to the lectures, by reviewing and presenting material on contemporary software engineering topics.

Business Intelligence

Business intelligence (BI) systems are applications and technologies for gathering, storing, analyzing, and accessing information for better business decision-making. The application areas of such systems include measuring and monitoring key performance indicators, benchmarking and forecasting sales, performing data mining and analysis of customer information.
This course provides a comprehensive introduction to the concepts, components, techniques and applications of BI and related areas. Students will gain awareness, in a gradual manner, of the complete life cycle of implementing and managing BI systems.

Year 2

Industrial Group Project

The purpose of this unit is to provide students with the opportunity to integrate and apply the skills and the knowledge they have acquired so far in their studies to a realistic problem. Students are exposed to the processes involved in the team-based development of software through real projects that are provided by companies from the software industry.

Research Skills and Dissertation Preparation

This unit intends to introduce students to the research topics and techniques that are commonly employed in software engineering and telecommunication science. Students will be exposed to the principles of report writing, literature reviewing, and research designs and approaches. These research approaches will include the design of data collection and analysis methods, as well as representation and interpretation of the results. An introduction to tools facilitating quantitative and qualitative analysis will also be provided. The unit is enriched with a number of exercises, case studies and discussions and concludes with practical guidelines of how to write a successful dissertation.

Data Mining and Machine Learning

This unit addresses the contemporary aspects, algorithms and challenges of mining big data by introducing data mining and machine learning algorithms. The unit aims to provide students with the necessary skills, knowledge and hands-on experience regarding data exploration, data preprocessing, visualization, unsupervised machine learning algorithms, clustering and other contemporary topics like dimensionality reduction, anomaly detection, pattern recognition, association rules and recommendation systems.

Advanced Artificial Intelligence Techniques

This unit starts with an in-depth introduction to Artificial Intelligence problem solving techniques and covers contemporary and advanced AI algorithms and application area. Main aspects and challenges that will be addressed by introducing relevant theory and hands-on practical sessions include search algorithms (brute-force, heuristic), supervised Learning Algorithms, Neural Networks, Deep Learning, Natural Language Processing and image classification.

Cloud Engineering

The unit aims at introducing students to a range of foundational –as well as more advanced– topics pertaining to cloud engineering, including:

• Fundamental concepts: foundations of cloud computing, principles of cloud computing, benefits, cloud service models, cloud deployment models, virtualisation technologies.
• Cloud platforms: AWS EC2, AWS Lambda, Heroku.
• Cloud-native applications: SOA, Microservices, Docker, Kubernetes, DevOps, challenges involved.
• Quality of provisioned services: scaling, IoT, Fog Computing, Mobile Edge Computing, SLAs.
• Security issues: access control, colocation attacks, side-channel attacks.

Summer of Year 2

Dissertation