Welcome to the git repository of TPM034A - Machine learning for socio-technical systems course. Here, you will find notebooks and sources for lab sessions and assignments related to the course. Please review the following information carefully.
Machine Learning (ML) is increasingly seen as a crucial part of the puzzle to solving the socio-technical challenges of today’s networked, urbanised, knowledge societies. Successful adoption of ML does, however, not only require skilled computer scientists that do the hard-core programming. Also, professionals need both domain knowledge of socio-technical systems and a profound understanding of ML.
This course aims to provide students in the socio-technical domain with a profound understanding of ML. It prepares students for the challenges and questions ML will pose to them in their later careers. To this end, the course consists of three parts:
- Part 1: Fundamentals of ML
- Part 2: Explainability of ML
- Part 3: Applications of ML for socio-technical challenges
In part 1, students learn about ML fundamentals and methods. These weeks a critical technical foundation is laid for grasping the strength and weaknesses of ML for the analysis and design of socio-technical systems.
Part 2 is devoted to the explainability of ML. For public decision-making as well as for decision-making in high-stake contexts, such as e.g. autonomous vehicles, legal systems’ transparency and explainability of the models are of critical importance. Students learn several popular explainability techniques and discuss their value for applications in socio-technical systems.
In Part 3, a rotating group of TPM scholars provides exemplar applications of ML in socio-tech systems. This serves two purposes: (1) to show where and how ML is applied for analysis and application in socio-technical systems, and (2) to deepen reflection on the impact of ML-based solutions and interventions on individuals, organisations, and society. Consecutively, students work in a group on a final project, building forth on one of the presented applications. This project brings together the three parts of this course. Students need to apply ML models and techniques to real-world data in a notebook, interpret, and communicate their results, taking into account the socio-technical setting through a presentation.
The course consists of oral lectures, lab sessions, and assignments. The aim of the lab sessions is to show and reinforce how the ML models, explainable ML techniques and ML ideas presented in the oral lectures are put to practice. Also, they help students gather hands-on machine learning skills. The lab sessions involve a series of exercises in the form of Jupyter notebooks.
The course consists of 2 oral lectures and 1 lab session per week. Attendance of the lectures and lab sessions is highly recommended to keep up with the course, but not mandatory.
We expect students have taken undergraduate courses on the following topics before coming to this course. – Statistics & Data analyses (e.g. hypothesis testing, correlation, etc.) – Python programming (basic level)
Please see below the schedule of the lectures and lab sessions, and the calendar of publication and deadlines.
| Week | Lecture | Weekday | Date | Time | Teacher | Lecture type | Topic | Materials |
|---|---|---|---|---|---|---|---|---|
| 46 | 0.0 | Sander | Lab session | L0: Basics of python and data analysis | Lab session 0 | |||
| 46 | 1.1 | Monday | 11-11-2024 | 13:45 - 15:45 | Sander | Opening | LOs, Set-up, Scope, Planning, Expectations; What is ML? | |
| 46 | 1.2 | Tuesday | 12-11-2024 | 10:45 - 12:45 | Sander | Oral lecture | Learning, Generalisation, Model dev, Regression, Geospatial data | |
| 46 | 1.3 | Friday | 15-11-2024 | 10:45 - 12:45 | Sander | Lab session | L1: Discover, explore and visualise data | Lab session 1 |
| 47 | 1.4 | Monday | 18-11-2024 | 13:45 - 15:45 | Sander | Oral lecture | Decision trees; Model performance metrics | |
| 47 | 1.5 | Tuesday | 19-11-2024 | 10:45 - 12:45 | Sander | Oral lecture | ANNs; training; tuning | |
| 47 | 1.6 | Friday | 22-11-2024 | 10:45 - 12:45 | Sander | Lab session | L2: Artificial Neural Networks | Lab session 2 |
| 48 | 1.7 | Monday | 25-11-2024 | 13:45 - 15:45 | Sander | Oral lecture | Ensembles; Random forest; XGBoost | |
| 48 | 1.8 | Tuesday | 26-11-2024 | 10:45 - 12:45 | Sander | Oral lecture | Embeddings, Causality; In vs out-of-distribution generalisation | |
| 48 | 1.9 | Friday | 29-11-2024 | 10:45 - 12:45 | Sander | Lab session | L3: Working with image embeddings | Lab session 3 |
| 49 | 2.1 | Monday | 02-12-2024 | 13:45 - 15:45 | Giacomo | Oral lecture | The risks of AI, Explainable AI, Transparency, Explanation by simplification | |
| 49 | 2.2 | Tuesday | 03-12-2024 | 10:45 - 12:45 | Giacomo | Oral lecture | Global model-agnostic methods (Partial Dependence Plots, Individual Conditional Expectation Plots), LIME, SHAP | |
| 49 | 2.3 | Thursday | 05-12-2024 | 08:45 - 10:45 | Giacomo | Lab session | L4: Explainable AI and energy prediction | |
| 50 | 2.4 | Monday | 09-12-2024 | 13:45 - 15:45 | Giacomo | Oral lecture | Other post-hoc explainability tools (GSA, PFI), Responsible AI, XAI and climate change, Recap | |
| 50 | 2.5 | Tuesday | 10-12-2024 | 10:45 - 12:45 | Giacomo | Lab session | L5: Explainable AI and appliance usage prediction | |
| 50 | 3.1 | Thursday | 12-12-2024 | 10:45 - 12:45 | Amir | Oral lecture | Pitches by TPM researchers for the mini-projects | |
| 51 | 3.2 | Monday | 16-12-2024 | 13:45 - 15:45 | Amir | Group work | ||
| 51 | 3.3 | Tuesday | 17-12-2024 | 10:45 - 12:45 | Amir | Group work | ||
| 51 | 3.4 | Thursday | 19-12-2024 | 10:45 - 12:45 | Amir | Group work | ||
| 02 | 3.5 | Monday | 06-01-2025 | 13:45 - 15:45 | Amir | Group work | ||
| 02 | 3.6 | Tuesday | 07-01-2025 | 10:45 - 12:45 | Amir | Group work | ||
| 02 | 3.7 | Thursday | 09-01-2025 | 10:45 - 12:45 | Amir | Presentations |
The assignments have to be submitted directly to Brightspace.
| Week | Weekday | Date | Time | Event | Materials |
|---|---|---|---|---|---|
| 46 | Friday | 08-11-2024 | 23:59 | Lab 0 publication | Lab session 0 |
| 46 | Monday | 11-11-2024 | 16:00 | Lab 1 publication | Lab session 1 |
| 46 | Wednesday | 13-11-2024 | 09:00 | Assignment 1 publication | Assignment 1 |
| 47 | Monday | 18-11-2024 | 16:00 | Lab 2 publication | Lab session 2 |
| 48 | Monday | 25-11-2024 | 09:00 | Deadline Assignment 1 | PASSED |
| 48 | Monday | 25-11-2024 | 16:00 | Lab 3 publication | Lab session 3 |
| 48 | Wednesday | 27-11-2024 | 09:00 | Assignment 2 publication | Assignment 2 |
| 49 | Wednesday | 04-12-2024 | 18:00 | Lab 4 publication | Lab session 4 |
| 50 | Monday | 09-12-2024 | 16:00 | Lab 5 publication | Lab session 5 |
| 50 | Wednesday | 12-12-2024 | 09:00 | Deadline Assignment 2 |
We use the Issues section as the Q&A platform of this course. Here, you can post your questions related to the content of the lectures, the lab sessions, assignments and technical problems with Python. Before you create a new issue, please make sure the issue has not been raised before by one of your classmates. Besides asking questions, you can comment on the earlier issues e.g. to continue the discussion. As an example, we have already created the first issue; see Issues.
To create a new issue (question, discussion or problem) in the course repository, follow these steps:
- Go to the "Issues" section of the course repository.
- Click on "New issue" in the green button located at the upper right corner of your screen.
- Add an informative title to your question or problem (e.g., "Train method from sklearn does not import in my notebook").
- Describe your issue clearly and concisely.
- Click on "Submit new issue" in the green button below the text description.
After that, the lecturer or teaching assistant will reply to your question. Also, you are allowed (even encouraged) to reply to questions posted by your fellow students! If you know how to help your fellow student with an issue, share your thoughts!
In this course, lab sessions and assignments will require programming in Python. You’ll need to prepare your computer by installing and setting up essential software in order to complete this course. Specifically, you will need a Python interpreter and a code editor. The interpreter allows your computer to run Python code, and it can be installed directly from Python’s website or through third-party software like Conda or Anaconda. The code editor is where you’ll write and execute Python scripts and notebooks; examples include VS Code, PyCharm or JupyterLab.
We offer instructions for two different setup options: (1) Anaconda, and (2) Google Colab. While you can do the activities with both, we strongly recommend Anaconda.
- Anaconda: This setup involves installing Anaconda (if it isn't already installed) and configuring the required Python packages for the course. This method is commonly used in other TU Delft courses, so it may be familiar to you.
- Google Colab: This option requires no installation on your computer, as you’ll work in an online environment. This makes setup easy, though it requires a stable internet connection and can sometimes be slower than the other options.
-
If you are unfamiliar with Python, we recommend completing lab session 0 after you finish the workspace setup. This lab provides the necessary tools to conduct the lab sessions. It covers topics such as data structures, utilising external libraries, data exploration, visualisation, etc.
-
For those with advanced experience in managing Python versions and environments, we also provide the
requirements.txtfile for you to create the respective environment. If you choose this method, ensure you create a virtual environment with Python version 3.11 for compatibility. This option is intended for students comfortable with Python’s native package manager, PIP, and manual environment setup.
- Download Anaconda for your system:
- Run the installer and follow the instructions.
- Once you have anaconda installed, we need to set up an independent virtual environment that isolates all the functionality we need in this course.
Python environments function like isolated sandboxes, each with its own versions of Python and packages. You can create, export, list, update, and remove environments as needed. Moving between environments, known as “activating” an environment, allows you to work with different setups for specific projects. When you’re finished with an environment, you can simply “deactivate” it to return to your default settings.
For this course, we have prepared the Python evironment as recipe. With this recipe, Anaconda can create the same coding environment for all of you. The recipe can be found in this repo (tpm034a_env.yaml).
- Download and unzip the
tpm034a_envfile to your computer (download link) - Open Anaconda Navigator:
- (1) Go to “Environments” in the left sidebar.
- (2) Click on “Import”
- (3) From your local drive, import the file you just downloaded (*.yaml)
- (4) Give the environment a name (e.g., tmp034a)
- (5) Keep the option “Overwrite exisiting environment” UNchecked.
- (6) Click on import (Depending on the speed of your connection, this step will take a while (but no less than 15-30 minutes))
- Once you have the environment ready. We have to choose an interpreter for activating the environment.
- Open Anaconda Navigator:
- (1) Go to “Home” in the left sidebar.
- (2) In the scroll menu, select your newly created environment (e.g., tmp034a).
- (3) Choose the interpreter of your preferences. Click on "install", and then "lunch". The course team suggest VS Code.
- If everything was installed properly, the interpreter of your preferences in now opening with the env we just created.
For this option, you need a Google account, Google Chrome web browser, and a stable internet connection. Please follow the steps we've included below to set up the workspace.
-
Step 1: Download or clone this repo to your computer.
- For downloading: on the top of this site, click on (1) <>Code tab, then in the green button (2) Code and then (3) Download ZIP (See numbers 1, 2, and 3 on the following image). Unzip this file into a working folder of your own choice.
- For cloning: Open a console (terminal on macOS or CMD on Windows), locate the console where you want to get this repo, and execute
git clone .... The entire repo will be downloaded to your computer. (IMPORTANT:You need to have installed GIT for cloning)
- For downloading: on the top of this site, click on (1) <>Code tab, then in the green button (2) Code and then (3) Download ZIP (See numbers 1, 2, and 3 on the following image). Unzip this file into a working folder of your own choice.
-
Step 2: Go to http://colab.research.google.com
-
Step 3: Sign in with your Google account (if you are already signed in, skip this step). If you do not have a Google account, you must (temporarily) create one.
-
Step 4: Upload the Python notebook you want to work on Colab. Click on the "Upload" tab and then on the "Choose file" tab, see numbers 1 and 2 in the figure below. Then, navigate to your working folder (Step 1) and select the Python notebook (.ipynb) you want to work on (e.g. lab_session_00.ipynb).
-
Step 5: Once open, click on "View" >> "Expand sections" on the menu bar.
-
Step 6: Importantly, Each notebook has a cell to prepare the data and environment in Google Colab. Uncomment (i.e. remove the '#') the lines related to the Colab set-up in your notebook, see the figure below. Run this cell and wait until finished.
-
Step 7: You are all set! You can work on your notebook.
Finally, note that the requirements files may be updated during the course to include more dependencies if needed.