ADS-AI
Year 1 - Foundation
  • Block D - Team-based Working
  • Block C - Data Modelling
  • Block B - Data Understanding and Preparation
  • Block A - Business Understanding
Year 2 - Role Orientation
  • Block A - Analytics Translator
  • Block C - Artificial Intelligence Scientist -Speech and Language
  • Block D - Engineer - MLOps
  • Block B - Artificial Intelligence Scientist - Vision
Contact Us
  • Ask me Anything
  • DataLab Absence
Markdown Sample Documentation
  • Example code Documentation
    • 1. Markdown Elements
    • 2. Toasts Card
    • 3. Code Blocks
    • 4. Mermaid Test
    • 5. Emoji Test
    • 6. Gist Test
    • 7. Avatar Test
    • 8. Mentions Test
    • 9. Fonts Test
    • 10. Mathjax Test
    • 11. Primer Utilities Test
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Content Documentation
  • Sociology
    • 1. Sociology (optional)
  • Agile Project Management
    • 1. The scrum approach to project success
    • 2. Essential roles for scrum teams
    • 3. Project vision and roadmaps
    • 4. Creating a Project Vision, Roadmap and Project Planning
    • 5. Sprit planning and standup meetings
    • 6. Wrapping up a sprint and retrospectives
    • 7. DataLab: Agile Scrum Project Management - Sprint Planning
  • Business Intelligence
    • Homework Exercises
    • 1. Data engineering: Data architecture & pipeline design
    • 2. SQL: Data Definition Language (DDL) clauses 1 & Data Manipulation Language (DML) clauses
    • 2. DataLab 1: Research design, EDA, and codebook
    • 2. Introduction to Business Intelligence
    • 4. SQL: Data Query Language (DQL) clauses
    • 5. DataLab 2: Database & SQL assignment
    • 5. SQL: Data Definition Language (DDL) clauses 2
    • 6. Getting started with Power BI & Prepare data for analysis with Power BI
    • 7. DataLab 1: Data wrangling & UX design in Power BI
    • 8. Data modeling in Power BI
    • 9. Data visualization in Power BI
    • 10. DataLab 2: Basic visuals & DAX in Power BI
    • 11. Data analysis & Managing workspaces/datasets in Power BI
    • 12. DataLab 1: Advanced visuals & Analyzing data in Power BI
  • Time Series
    • 1. Time Series (optional)
  • Data Science
    • 1. Quantifying our World into Data
    • 2. Intro to R
    • 3. Datalab 00: SDG indicators
    • 5. Intro to Variables and Dataframes
    • 6. Descriptive Analyses & Visualisations
    • 7. Datalab 01: Exploratory Data Analysis
    • 8. Introduction to Probability 1
    • 9. Datalab 02: Findings & Data Visualisations
    • 10. Intro to Probability 2
    • 11. Analysing Relationships between Variables
    • 12. Datalab 03: Discussion
    • 13. Reporting and Visualising Data
    • 14. Datalab 04: Conclusion and Referencing
    • 15. Explanatory versus Predictive Modelling Approach
    • 16. Block A Recap
    • 17. Datalab 05: Poster Presentation Day
  • Mathematics I: Linear Algebra and AI
    • 1. Introduction to Linear Algebra
    • 2. Elementary Operations on Matrices
    • 3. Matrix Multiplication and inversion
    • 4. Data Lab 1: Elementary Operations on Matrices
    • 5. Linear algebra applied to Linear Systems
    • 6. Linear Algebra and Linear models
    • 7. Data Lab 2: Creative Brief
    • 7. Data Lab 3: Creative Brief
    • 8. Linear Algebra and Image Processing I
    • 10. Linear Algebra and Image Processing II
  • Introduction to Machine Learning
    • 1. Supervised and Unsupervised Learning
    • 2. Regression Algorithms
    • 3. Datalab 00: Regression Algorithms
    • 4. Classifications Algorithms
    • 5. Datalab 01: Classification Algorithms
    • 6. Decision Trees
    • 7. Supervised Learning: A Recap
    • 8. Datalab 02: Tree-Based Analyses
    • 9. Clustering: K-Means
    • 10. Datalab 03: Clustering Analyses
    • 11. Perceptron algorithm
    • 12. Datalab 04: Perceptron
    • 15. Final Assignment: Machine Learning Analysis
  • Responsible and Explainable AI
    • 1. Fairness & Bias: Definitions
    • 2. DataLab 1: Implicit bias & A Designer’s Critical Alphabet
    • 3. Fairness & Bias: Individual fairness, debiasing techniques & toolkits
    • 4. Fairness & Bias: Group fairness metrics
    • 5. DataLab 2: Fairness metrics, and debiasing techniques for image data
    • 7. Responsible & Explainable AI
    • 8. XAI: The need for explanations!
    • 9. Explaining neural networks
    • 10. DataLab 1: XAI
    • 11. Feature Attribution in Computer Vision
    • 12. Moving beyond feature attribution
    • 13. DataLab 2: XAI
  • Artificial Intelligence
    • 1. Foundations of AI (1): Philosophy, and history of AI
    • 2. Turing Test & Chatbots (1): Article
    • 3. AI in Science Fiction (1): Minority Report & Outline
    • 4. Turing Test & Chatbots (2): Discussion & Turing Test
    • 5. Foundations of AI (2): Symbolic/non-symbolic AI & Intelligent agents
  • Human-Centered Artificial intelligence
    • 1. Interaction & Information Processing Fundamentals
    • 2. Datalab 13: Risk-Assessment of Disruptive Technologies
    • 3. Interaction Design
    • 4. Interface Design
    • 5. Datalab 14: Conceptualizing a Wireframe Prototype
    • 6. User Testing
    • 7. Datalab 15: Introduction to A/B-Testing
    • 8. User-Centered Design for AI
    • 9. Designing for AI Algorithms Implementation
    • 10. Datalab 16: Client Testing of Wireframe Prototypes
    • 15. Final Assignment: Application Prototype
  • Deep Learning
    • 1. Introduction to Deep Learning Project
    • 2. Data Structures for Deep Learning
    • 3. Data Lab 3: Tensors
    • 4. Perceptron algorithm
    • 5. Introduction to Neural Networks
    • 6. Data Lab 4: Neural Network in Python
    • 7. Implementing Neural networks using TensorFlow
    • 8. Data Lab 5: Neural Network in Tensorflow
    • 9. Neural Network for classification
    • 10. Neural Network in Google Playground
    • 11. Data Lab 6: Classification using TensorFlow (MNIST datasets)
    • 12. Image Classification (CNN using keras)
    • 13. 8.1 DLab-CreativeBrief.md
    • 14. Convolutional Neural Networks - Deep Dive
    • 15. Data Augmentation
    • 16. Data Lab - 7,8,9,10 : Working on Creative Brief
    • 18. Transfer Learning
    • 19. Practical Issues & BestPractices
  • Geospatial Analytics
    • 1. Workshop: Building a weather station
    • 1. Geospatial analytics (optional)
  • Programming in Python
    • 1. Programming: A Primer
    • 2. Setting up your development environment
    • 3. Python for AI and Data Science
    • 4. Data Lab 1: Python Foundation exercises
    • 5. Python data types & data structures
    • 6. Python: Pandas
    • 7. Python: NumPy
    • 8. Data Lab 2: Python data structures exercises
    • 9. Python: Image processing
    • 10. Data Lab: Python Image Steganography
    • 11. Advanced NumPy: Broadcasting and Vectorizing
    • 12. Python: Matplotlib
    • 13. Data Lab: Python Image Steganography
    • 14. Python: Coding standards and reproducible data science
    • 15. Data Lab: Python Image Steganography
    • 16. Python - Recap of key concepts.
    • 17. Python (web) application development
    • 18. Data Lab: Python Image Steganography
  • Digital Transformation
    • 1. AI for Business (1): Introduction into application of AI in businesses & Risks and benefits of AI
    • 1. Scientific writing (2): Citation & Mechanics of style of effective writing.
    • 1. Scientific writing (1): The structure of a paragraph and rules of writing & Bias-free language
    • 2. AI for Business (2): Application of AI by businesses
    • 4. Legal (2): The Proposed Artificial Intelligence Act
    • 4. AI in Science Fiction (3): Quizzes on Scientific writing. Presentations on the first draft of the report
    • 4. AI in Science Fiction (4): Quiz on Scientific writing. Q&A session on the report
    • 4. DataLab (1): Debates: Ethical Decision-Making
    • 4. DataLab (2): Debates: Ensuring Responsible AI is unrealistic.
    • 4. DataLab (1): Debates: Companies that implement the GDPR law have a competitive advantage.
    • 4. DataLab (2): Formative Feedback Session.
    • 4. Ethics & Law (1): Introduction to DEDA Framework. Preparation for debates: Ethical Decision-Making.
    • 4. Ethics & Law (2): Ethical Guidelines for Statistical Practice. Introduction to GDPR.
    • 4. Ethics & Law (3): Preparation for debates: Can AI Systems Be Ethical?
    • 4. Legal (1): Introduction to the European Union Law-Making Process.
    • 4. Legal (3): When GDPR meets AI
    • 7. Ethics & Law
ADS-AI
  • Study Content
  • Business Intelligence
  • README.md

Business Intelligence

  • Homework Exercises
  • Data engineering: Data architecture & pipeline design
  • SQL: Data Definition Language (DDL) clauses 1 & Data Manipulation Language (DML) clauses
  • DataLab 1: Research design, EDA, and codebook
  • Introduction to Business Intelligence
  • SQL: Data Query Language (DQL) clauses
  • DataLab 2: Database & SQL assignment
  • SQL: Data Definition Language (DDL) clauses 2
  • Getting started with Power BI & Prepare data for analysis with Power BI
  • DataLab 1: Data wrangling & UX design in Power BI
  • Data modeling in Power BI
  • Data visualization in Power BI
  • DataLab 2: Basic visuals & DAX in Power BI
  • Data analysis & Managing workspaces/datasets in Power BI
  • DataLab 1: Advanced visuals & Analyzing data in Power BI
Next

2023,Applied Data Science and Artificial Intelligence @ Breda University of Applied Sciences
Contact : Frank Peters