Learn Artificial intelligence in Los Angeles, California

Master of Science in Computer Science with Specialization in Intelligent Robotics.

Looking at artificial intelligence methods and techniques, this course explores autonomous movement, path finding, decision making, genre considerations and learning with dynamic programming.

The student will learn the terminology and methods used in a variety of artificial intelligence (AI) areas. These topics will be covered: history of artificial intelligence, search techniques, knowledge representation. In addition, one or more of these topics will be covered: expert systems, uncertainty, case-based reasoning, neural networks, vision, robotics. The student may use various AI tools, Lisp, and/or Prolog for AI projects.

An introduction to the large and diverse field of artificial intelligence. Topics include: problem-solving by search and constraint satisfaction; alpha-beta search for two-player games; and logic and knowledge representation, planning, learning, decision theory, statistical learning, and computer vision.

Introduction and general pattern recognition concerns and statistical pattern recognition: introduction to statistical pattern recognition, supervised learning (training) using parametric and nonparametric approaches, parametric estimation and supervised learning, maximum likelihood (ML) estimation, the Bayesian parameter estimation approach, supervised learning using nonparametric approaches, Parzen windows, nonparametric estimation, unsupervised learning and clustering, and formulation of unsupervised learning problems; syntactic pattern recognition: quantifying structure in pattern description and recognition, grammar-based approach and applications, elements of formal grammars, syntactic recognition via parsing and other grammars, graphical approaches, and learning via grammatical inference; neural pattern recognition: the artificial neural network model, introduction to neural pattern associators and matrix approaches, multilayer, feed-forward network structure, and content addressable memory approaches. The Hopfield approach to pattern recognition, unsupervised learning, and self-organizing networks.

An introduction to the field of Computer Vision, focusing on the underlying algorithmic, geometric and optic issues. The course starts with a brief overview of basic image processing topics (convolution, smoothing, edge detection). It then proceeds on various image analysis topics: binary images, moments-based shape analysis, Hough transform, image formation, depth and shape recovery, photometry, motion, classification, special topics.

Artificial Intelligence History
Intelligent Agents

Problem Solving
Solving Problems by Searching
Beyond Classical Search
Adversarial Search
Constraint Satisfaction Problems

Knowledge and Reasoning
Logical Agents
First-Order Logic
Inference in First-Order Logic
Classical Planning
Planning and Acting in the Real World
Knowledge Representation

Uncertain Knowledge and Reasoning
Quantifying Uncertainty
Probabilistic Reasoning
Probabilistic Reasoning over Time
Making Simple Decisions
Making Complex Decisions

Learning
Learning from Examples
Knowledge in Learning
Learning Probabilistic Models
Reinforcement Learning

Communicating, Perceiving, and Acting
Natural Language Processing
Natural Language for Communication
Perception
Robotics

Conclusions
Philosophical Foundations
AI: The Present and Future

The purpose of this course is to cover the major paradigms of Artificial Intelligence programming with a bias towards the application of these techniques to computer games. It is an advanced foundational course from which individual will be able to discover their own preferences and avenues for further exploration. In addition to covering the key AI programming paradigms the course also discusses

selection of appropriate programming languages and representational schemes
persistence , history and rollback to earlier states
personality, emotion, motivation and psychology as they apply to computer games
During the course attendees will have access to a large library of books, software and research papers that they are encouraged to explore.

Key Skills

Ability to choose between various approaches and frameworks for adding AI capabilities to computer games
Insights into the complexity and performance tradeoffs of different AI technologies and techniques
Techniques for developing games that learn and evolve
Course Contents
Overview of basic physics and behaviours
movement algorithms, statics and kinematics
searching a... [Read More]