Catalog Description: Ideas and techniques underlying the design of intelligent computer systems. Topics include search, game playing, knowledge representation, inference, planning, reasoning under uncertainty, machine learning, robotics, perception, and language understanding.
Units: 4
Prerequisites: COMPSCI 61A, COMPSCI 61B, and COMPSCI 70.
Formats:
Summer: 6.0 hours of lecture and 2.0 hours of discussion per week
Spring: 3.0 hours of lecture and 1.0 hours of discussion per week
Fall: 3.0 hours of lecture and 1.0 hours of discussion per week
Grading basis: letter
Final exam status: Written final exam conducted during the scheduled final exam period
Class Schedule (Fall 2022):
TuTh 17:00-18:29, Wheeler 150 –
Dr. Igor Mordatch, Peyrin Kao
Class Schedule (Spring 2023):
TuTh 17:00-18:29, Wheeler 150 –
Peyrin Kao, Stuart RUSSELL
Department Notes:
Course objectives: An introduction to the full range of topics studied in artificial intelligence, with emphasis on the "core competences" of intelligent systems - problem solving, reasoning, decision making, and learning - and on the logical and probabilistic foundations of these activities.
Topics covered:
- history
- intelligent agents
- uninformed search
- informed search
- constraint satisfaction
- game-playing
- logical agents
- propositional logic
- first-order logic
- inference in first-order logic
- resolution, logic programming
- planning, plan execution
- uncertainty, probability theory, probabilistic inference
- Bayesian networks and associated inference algorithms
- optimal decisions under uncertainty
- optimal sequential decisions, Markov decision processes
- learning agents
- inductive learning, decision trees
- neural networks
- Bayesian learning
- natural language processing
- perception/vision
- robotics
- philosophical foundations
Related Areas: