Grid Representations in Video Game AI - Lecture 5
Explore grid representations in video game AI, covering localization, optimization, pathfinding, and hierarchical structures for efficient game world modeling and navigation.
Introduction to Artificial Intelligence - Game Theory - Lecture 9
Explore game theory concepts and applications in AI, focusing on advanced strategies and decision-making in multi-agent environments.
Game Programming: Collision Detection - Lecture 8
Explore collision detection techniques in game programming, covering algorithmic approaches and data structures for efficient collision handling.
Introduction to Artificial Intelligence - Connect 4 and Alpha-Beta Pruning - Lecture 11
Explore Connect 4 AI implementation, focusing on UI controls, game state management, player strategies, and evaluation functions. Learn to develop and debug an intelligent game-playing agent.
Introduction to Artificial Intelligence - Lecture 13: Assignment 4
Explore algorithmic techniques and data structures for AI problem-solving, applying learned concepts to simple games through hands-on assignments.
COMP3200 - Introduction to Artificial Intelligence - MDP and Dynamic Programming - Lecture 16
Explore Markov Decision Processes and Dynamic Programming in AI, learning algorithmic techniques for modern problem-solving in game environments.
Intro to Artificial Intelligence - RL Monte Carlo Methods - Lecture 17
Explore Monte Carlo methods in reinforcement learning, applying algorithmic techniques to solve AI problems in gaming environments.
Introduction to Artificial Intelligence: Connect 4 Assignment and AI Techniques - Lecture 12
Explore AI techniques for Connect 4, including GUI controls, game state management, player implementations, and advanced algorithms like Alpha-Beta pruning and Zobrist hashing.
Introduction to Artificial Intelligence - Lecture 14: Assignment 4
Explore algorithmic techniques and data structures for AI problem-solving, applying learned concepts to simple games in this graduate-level introduction to Artificial Intelligence.
Introduction to Artificial Intelligence - Lecture 15: Intro to Reinforcement Learning
Explore reinforcement learning fundamentals, including key concepts and applications in AI problem-solving environments for modern algorithmic techniques.
Introduction to Artificial Intelligence - Bandit Algorithms - Lecture 16
Explore bandit algorithms in AI, learning key techniques for optimizing decision-making in uncertain environments through practical applications and examples.
Intro to Artificial Intelligence - Markov Decision Process (MDP) - Lecture 17
Explore Markov Decision Processes (MDPs) in AI, learning algorithmic techniques for modern problem-solving in uncertain environments.
Introduction to Deep Neural Networks - Lecture 21
Explore deep neural networks, their architecture, and applications in AI. Learn key concepts and techniques for implementing advanced machine learning models.
Is C++23 std::mdspan a Zero-overhead Abstraction? - Lecture
Explore C++23 std::mdspan's efficiency as a zero-overhead abstraction for multi-dimensional arrays through matrix and image operations, comparing assembly output to low-level implementations.
Leveraging the Power of C++ for Efficient Machine Learning on Embedded Devices
Explore practical C++ and TensorFlow Lite integration for efficient machine learning on embedded devices. Includes image classification and hand gesture recognition demonstrations on Raspberry Pi.
Parameter Inference of Music Synthesizers with Deep Learning
Explore deep learning techniques for parameter inference in music synthesizers, enhancing sound design efficiency and creativity for producers and audio professionals.
Deep Learning in Audio for Absolute Beginners: From No Experience to a Deployed Model
Jumpstart deep learning in audio: from basics to deploying models. Learn myths, dataset creation, PyTorch training, and real-time plugin deployment for Virtual Analog modeling of audio effects.
Deep Learning in Audio for Absolute Beginners: From No Experience to a Deployed Model
Jumpstart deep learning in audio: from basics to deploying models. Learn myths, fast techniques, dataset creation, PyTorch training, and real-time plugin deployment for Virtual Analog modeling.
Developing an AI-Powered Karaoke Experience
Explore AI-powered karaoke development, including audio source separation, voice transcription, and real-time remixing. Learn challenges and solutions for mobile and web platforms.
Collaborative Songwriting and Production with Symbolic Generative AI
Explore cutting-edge techniques for integrating generative AI into collaborative songwriting and music production. Learn about symbolic generation, AI-generated MIDI, and innovative approaches to augment creativity.
Inference Engines and Audio - Machine Learning in Audio Applications
Explore inference pipelines in machine learning for audio applications, including common engines, their functionality, and creating custom solutions for platforms like Cmajor.