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Undergraduate course, UC Berkeley, EECS, 1900
Teaches core algorithmic concepts in Computer Science, including Divide-and-Conquer, Graph, Greedy Algorithms, Dynamic Programming, Linear Programming, Reductions, P vs. NP, Streaming, etc.
Undergraduate course, UC Berkeley, EECS, 1900
Overviews developments in AI, from searching algorithms to state-of-the-art RL and neural networks.
Undergraduate course, UT Austin, CS, 1900
This course covers the basic building blocks and intuitions behind designing, training, tuning, and monitoring of deep networks. It covers both the theory of deep learning, as well as hands-on implementation sessions in pytorch. It also covers a series of application areas of deep networks in: computer vision, sequence modeling in natural language processing, deep reinforcement learning, generative modeling, and adversarial learning.
Undergraduate course, UT Austin, CS, 1900
This course provides a broad introduction to artificial intelligence. Topics include: problem solving, including search and game playing; knowledge and reasoning, including inference planning; reasoning under uncertainty; machine learning