Week 1
-Lecture 1: introduction, course structure, “What is artificial intelligence?” and Turing Test
-Lecture 2: human brain architecture vs. Von Neumann computer architecture, brief history of AI, recent developments in AI, factors in AI progress; homework 1 assigned
Week 2
-Lecture 1: intelligent agents, environmental representation, percepts, intelligent agent models
-Lecture 2: setting goals in intelligent agents, measuring performance, predicting performance, utility functions, preference logic; homework 1 due, homework 2 assigned
Week 3
-Lecture 1: types of problems intelligent agents need to solve, constraint satisfaction problems, optimization problems, convexity vs. non-convexity, NP-hard problems
-Lecture 2: intro to problem solving techniques, simplex algorithm, classical search (breadth-first, depth-first, greedy); homework 2 due, homework 3 assigned
Week 4
-Lecture 1: heuristic search techniques, A* search, local search (hill-climbing, beam search), simulated annealing, exploration versus exploitation
-Lecture 2: evolutionary algorithms (evolutionary programming, evolution strategies, genetic algorithms); homework 3 due, homework 4 assigned
Week 5
-Lecture 1: particle swarm, ant-colony optimization, tabu search, no free lunch theorem
-Lecture 2: lab day #1—solving traveling salesman problem (TSP) with hill-climbing, tabu search, ant-colony optimization, genetic algorithms, and evolutionary programming; homework 4 due, homework 5 assigned
Week 6
-Lecture 1: knowledge bases, logical reasoning, and knowledge manipulation; select final project
-Lecture 2: Bayesian networks, fuzzy logic & hidden Markov models; homework 5 due
Week 7
-Lecture 1: midterm review
-Lecture 2: midterm exam
Week 8
-Lecture 1: introduction to machine learning, supervised learning, decision trees, k-Nearest Neighbors; homework 6 assigned
-Lecture 2: neural networks, linear discriminants, support-vector machines, kernel methods
Week 9
-Lecture 1: challenges in machine learning, noisy data, overfitting, model regularization, testing, validation, cross-validation; homework 6 due, homework 7 assigned
-Lecture 2: lab day #2—training support-vector machine on sample data-set using grid search and cross-validation to select level of model complexity and regularization coefficient (then replace grid search with genetic algorithm)
Week 10
-Lecture 1: computer vision, deep learning; homework 7 due, homework 8 assigned
-Lecture 2: decision theory, unsupervised learning, reinforcement learning
Week 11
-Lecture 1: overview of planning, planning-graphs, hierarchical task network planning, multi-agent planning; homework 8 due
-Lecture 2: overview of communication and coordination in intelligent agents, human-AI communication, AI-AI communication
Week 12
-Lecture 1: overview of AI engineering, building intelligent agents from AI building blocks, troubleshooting AI
-Lecture 2: areas of active research and development in AI
Week 13
-Lecture 1: future of AI, philosophical questions, AI’s challenges to humanity (e.g. labor dislocation, AI singularlity)
-Lecture 2: Final Project Presentations
Week 14
-Lecture 1: Final Project Presentations (final project paper due)
-Lecture 2: Final Exam review
Week 15
-Final Exam