Навыки, которые вы получите:
Artificial intelligence Python
Learn essential Artificial Intelligence concepts from AI experts like Peter Norvig and Sebastian Thrun, including search, optimization, planning, pattern recognition, and more.
- Basic knowledge of linear algebra and calculus.
- The ability to apply basic probability and statistics.
- Programming experience in Python.
- Experience implementing computer science algorithms and object-oriented programming.
- The ability to run programs and interpret output from a command line terminal or shell.
- You will also need access to a Windows, macOS, or Linux computer with Python 3.4 or later installed, and admin permissions to install new programs.
For those who want to
- Learn to write programs using the foundational AI algorithms powering everything from NASA’s Mars Rover to DeepMind’s AlphaGo Zero.
- Learn classical AI algorithms applied to common problem types.
- Master Bayes Networks and Hidden Markov Models, and more.
- Introduction to Artificial Intelligence.
- Classical Search.
- Automated Planning.
- Optimization Problems.
- Adversarial Search.
- Fundamentals of Probabilistic Graphical Models.
What will you learn
- The foundations of AI.
- Classical graph search algorithms--including uninformed search techniques like breadth-first and depth-first search and informed search with heuristics including A*.
- To represent general problem domains with symbolic logic and use search to find optimal plans for achieving your agent’s goals. Planning & scheduling systems power modern automation & logistics operations, and aerospace applications like the Hubble telescope & NASA Mars rovers.
- Iterative improvement optimization problems and classical algorithms emphasizing gradient-free methods for solving them.
- How to search in multi-agent environments (including decision making in competitive environments) using the minimax theorem from game theory.
- To use Bayes Nets to represent complex probability distributions, and algorithms for sampling from those distributions.
- The algorithms used to train, predict, and evaluate Hidden Markov Models for pattern recognition.