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
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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
Year2
BlockD
README.md
Block D - Engineer - MLOps
2023,Applied Data Science and Artificial Intelligence @ Breda University of Applied Sciences
Contact :
Frank Peters