Museum fire Detection

TDDE15 - Advanced Machine Learning course at Linkoping University, Sweden

This repository contains a collection of lab exercises and exams from the TDDE15: Advanced Machine Learning course taking at Linköping Univerity during the fall 2024. The main topics are: Bayesian Networks, Hidden Markov Models, Q-learning, REINFORCE, and Gaussian Processes.

This project aimed at inferring whether a patient has cancer based on their data. It consists of 2 applications: one for learning and one for predicting.

News segmentation by Bayes classifier.

Python code to compute full joint distributions of bayesian network

Practical experience in hyperparameter tuning techniques using the Keras Tuner library. Hyperparameter tuning plays a crucial role in optimizing machine learning models, and this project offers hands-on learning opportunities. Exploring different hyperparameter tuning methods, including random search, grid search, and Bayesian optimization

TPS ET RAPPORTS MODULE RCR 1 et 2

A collection of AI algorithms and techniques covering intelligent agents, search strategies (BFS, DFS, A*), probabilistic reasoning (Bayesian networks, HMM), neural networks (feed-forward, Hopfield), and reinforcement learning. Topics include adversarial search, alpha-beta pruning, decision trees, Markov processes, and game theory applications.

The homeworks related to Machine Learning university course would be saved here.

Artificial intelligence course projects

This repository contains the assignments of Artificial Intelligence course at Sharif University of Technology.

Finding d-separated nodes in a belief network

This project is a simple text processing using bayesian networks and bag of words(BOW) algorithm.

Case Study: Personalized Anti-Coagulation: Optimizing Warfarin Management Using Genetics and Simulated Clinical Trials

My introduction to Causality. I used a breast cancer dataset and tried to classify cell types by using the causal relationship between the cell features.

First implementation of NormAN, an agent-based framework for normative argument exchange across networks.

Official implementation of the AIAA Journal paper "Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models"