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Explore fundamental artificial intelligence principles and techniques through this comprehensive lecture series from Stanford University's CS221 course. Master core machine learning concepts including linear regression, classification, stochastic gradient descent, neural networks, and backpropagation while developing expertise in search algorithms, dynamic programming, and A* search. Delve into advanced topics such as Markov Decision Processes, reinforcement learning, game theory with minimax and alpha-beta pruning, and constraint satisfaction problems with various solving techniques. Study probabilistic models through Markov networks, Bayesian networks, Gibbs sampling, and the EM algorithm, then advance to logic-based AI systems covering propositional and first-order logic with inference rules and resolution methods. Gain insights into contemporary AI challenges through specialized talks on AI and law, robustness in machine learning, robotics automation, healthcare inequality solutions, natural language processing, and critical issues like AI alignment, reward hacking, encoding human values, and algorithmic distribution effects.
