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There will be in total three assignments for this course. The format of these assignments are 3 jupyter notebook with blocks of codes that you can run and with several TODOs, but also eventually some tasks that needs to be done and you will need to submit an individual report for each one of these assignments. These assignments needs to be submitted via ANS Delft and we will soon add the links. The deadlines are the following ().

The topic of the assignments are the following:

1. Loading datasets and data processing. Running a couple of methods for classification, regression and unsupervised learning
   1. data processing and analysis
   2. data visualization
   3. training classifiers/regressors
   4. finding interesting patterns in data
   5. evaluate the performance of your models
2. Neural networks and modern AI for representation learning
   1. data processing and analysis, visualization
   2. design a Neural network and check all the necessary steps (loss functions, optimization and training)
   3. evaluation of the model
   4. Visualization of the results
   5. Representation learning
3. Explainability and bias in data (AI alignment?)
   1. LIME algorithm
   2. Code on Bias and textual data
   3. Maybe some info on AI and alignment

The data that we will use in these assignments are a various of standard datasets like: MNIST, Iris Dataset, Heart Disease and CIFAR10.

The first assignment can be found in here

The second assignment can be found in here

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