Projects for the Probabilistic Artificial Intelligence course at ETH Zürich 📚

Please note that the notebooks were used as exploratory tools 🔍. The solution.py files were designed to pass the baselines for the Probabilistic Artficial Intelligence Projects during the Autumn semester 2021. 🍂

✅ Task 1 - GAUSSIAN PROCESS REGRESSION ⚙️: To solve this problem, we have mostly used Python and the sklearn library.

✅ Task 2 - BAYESIAN NEURAL NETS 🧠: To solve this problem, we have mostly used Python and the PyTorch library.

✅ Task 3 - BAYESIAN OPTIMIZATION 🌠: To solve this problem, we have mostly used Python and the sklearn library.

✅ Task 4 - REINFORCEMENT LEARNING 🧮: To solve this problem, we have mostly used Python and the PyTorch library.