The twin goals of knowledge-based artificial intelligence (AI) are to build AI agents capable of human-level intelligence and gain insights into human cognition.
Overview
Syllabus
- Introduction to Artificial Intelligence
- Build a strong foundation in artificial intelligence by exploring core concepts from both a theoretical and practical perspective. This course blends essential principles with real-world problem-solving.
- Classical Search
- Learn classical graph search algorithms--including uninformed search techniques like breadth-first and depth-first search and informed search with heuristics including A*. These algorithms are at the heart of many classical AI techniques, and have been used for planning, optimization, problem solving, and more. Complete the lesson by teaching PacMan to search with these techniques to solve increasingly complex domains.
- Automated Planning
- Learn 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.
- Optimization Problems
- Learn about iterative improvement optimization problems and classical algorithms emphasizing gradient-free methods for solving them. These techniques can often be used on intractable problems to find solutions that are "good enough" for practical purposes, and have been used extensively in fields like Operations Research & logistics. Finish the lessons by completing a classroom exercise comparing the different algorithms' performance on a variety of problems.
- Adversarial Search
- Learn how to search in multi-agent environments (including decision making in competitive environments) using the minimax theorem from game theory. Then build an agent that can play games better than any human.
- Fundamentals of Probabilistic Graphical Models
- Learn to use Bayes Nets to represent complex probability distributions, and algorithms for sampling from those distributions. Then learn the algorithms used to train, predict, and evaluate Hidden Markov Models for pattern recognition. HMMs have been used for gesture recognition in computer vision, gene sequence identification in bioinformatics, speech generation & part of speech tagging in natural language processing, and more.
- After the AI Nanodegree Program
- Once you've completed the last project, review the information here to discover resources for you to continue learning and practicing AI.
- Extracurricular
- Additional lecture material on hidden Markov models and applications for gesture recognition.
Taught by
Ashok Goel
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Reviews
3.0 rating, based on 2 Class Central reviews
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This course covers an interesting range in material in a structured way that is easy to follow.
Unlike many of the other more "hip" AI MOOCs out there, the lectures for this one are rather dry and difficult to follow. It feels a lot more like a traditional "classroom" experience than some other MOOCs I have taken lately.
Even given the boring lectures, there is a large amount of content and a heavy use of graphics to demonstrate key points.
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Too hypothetical. Could use practical programming exercises, like python notebook style that Andrew Ng does in his Coursera courses. The examples sometimes are a bit far of from being applicable. Otherwise it is very interesting and would do well with a deepening follow up course.
