A problemset for uncertainty quantification and knowledge injection into reinforcement learning and active inference agents

Bayesian Optimization

Core Machine Learning concepts , algorithms & maths implemented completely from scratch using Python & numpy only.

Exercises in the subject "Probabilistic Machine Learning" at Hasso Plattner Institute.

Exploration of major kinds of statistical learning models and algorithms used in data analysis. Clustering, Neural Networks, Probabilistic ML are a few of the topics.

Final Project from the course "Probabilistic Machine Learning" @ Data Science & Scientific Computing, University of Trieste, year 2020/2021, written in ipynb.

Some notes on Probabilistic models and advanced ML methods. Implementation of RBM, Contractive AE, Denoising AE and some TS analysis

Machine Learning Code as per my understanding

Headquarters of the APRIL research lab

We present a probabilistic model for neural spike counts that can capture arbitrary single neuron and joint statistics with their modulation by external covariates.

Seminar in Probabilistic Machine Learning

Predicting air pollution amounts in cities using a Gaussian Process model

List of casual implementations of machine learning models from scratch.

Probabilistic modeling through Bayesian inference using PyStan with demonstrative case study experiments from Christopher Bishop's Model-based Machine Learning.

[AAAI 2019] "A Probabilistic Derivation of LASSO and L12-Norm Feature Selections", Di Ming, Chris Ding, Feiping Nie