ZD_2_10 — Speech Recognition and Synthesis: From Acoustic Models to Neural Voice Generation

Speech recognition (Automatic Speech Recognition — ASR) and speech synthesis (Text-to-Speech — TTS) are complementary technologies that bridge human spoken language and machine processing. ASR converts spoken audio into

ZD_2_01 — Machine Learning Mathematics

Machine learning — the science of algorithms that improve through experience — rests on a rich mathematical foundation spanning optimization, statistics, linear algebra, probability, and functional analysis. The core mat

ZD_2_14 — Autonomous Systems: Self-Driving Vehicles, Drones, and Safety-Critical AI

Autonomous systems are machines capable of performing complex tasks in unstructured, dynamic environments with limited or no human intervention — perceiving their environment through sensors, making decisions through com

ZD_2_00 — AI Machine Learning: Subfolder Summary

ZD_2_13 — Explainable AI: Interpretability, Trust, and the Black Box Problem

Explainable AI (XAI) is the field concerned with making artificial intelligence systems — particularly complex machine learning models — understandable to humans. As AI systems increasingly make or influence high-stakes

ZD_2_09 — Recommender Systems: Collaborative Filtering, Content-Based, and Hybrid Approaches

Recommender systems (RecSys) are algorithms and architectures that predict user preferences and suggest relevant items — products, movies, music, news articles, social media posts, job listings, potential partners — from

ZD_2_11 — Reinforcement Learning: Agents, Rewards, and Sequential Decision-Making

Reinforcement learning (RL) is a paradigm of machine learning in which an agent learns to make sequential decisions by interacting with an environment, receiving rewards (or penalties) for its actions, and adjusting its