CS B553: A LGORITHMS FOR O PTIMIZATION AND L EARNING aka “Neural and Genetic Approaches to Artificial Intelligence” Spring 2011 Kris Hauser

T ODAY ’ S A GENDA Topics covered Prerequisites Class organization & policies Coursework Math review

W HAT IS O PTIMIZATION ? The problem of choosing the “best” solution from some set of candidate solutions Airplane wing that minimizes drag Stock portfolio that maximizes return on investment Feedback control strategy with highest probability of picking up an object (In many problems, it is easier to measure the quality of candidate solutions than to produce the optimum!) A mathematical discipline that is heavily studied and utilized in other fields Powerful idea in AI, machine learning, computer vision, engineering, economics, applied sciences

O PTIMIZATION L EARNING O BJECTIVES Hands-on experience in specifying mathematical optimization problems Defining objective functions, constraints Identifying problem characteristics (e.g., convexity) Characteristics of small/medium/large scale problems Mostly continuous optimization, some discrete and mixed-integer optimization Solving optimization problems in practice Algorithms: descent-based, simplex based, stochastic Software packages Performance tricks Applied to realistic scenarios

W HAT IS L EARNING ? Deriving “meaningful” quantities from raw data (e.g., gathered from logs, surveys, sensors) and employing them Diagnosing a patient from reported symptoms Recognizing human activity from video Forecasting weather or economic behavior from history Diverse range of learning tasks, most of which involve one or more of: Fitting a model by adjusting model parameters Selecting a model structure that explains the data Using a model to infer meaningful quantities Many learning tasks are essentially optimization problems!

L EARNING L EARNING O BJECTIVES Conceptual frameworks for large scale learning Graphical models (e.g., Bayesian networks) Hidden Markov Models (HMMs), Dynamic Bayesian Networks (DBNs) Understanding of key components for implementing many learning algorithms Belief propagation Expectation maximization algorithms Monte Carlo techniques Experience applying algorithms to real-world datasets

O RGANIZATION Lectures, readings Lecture notes for optimization unit Probabilistic Graphical Models: Principles and Techniques (Koller and Friedman) for learning unit In-class group exercises

C OURSE WORK Attendance and participation: 20% of grade 8 homework assignments (4 written, 4 programming): 80% of grade Programming in language of your choosing Optional final project Original research, survey, or reproduction of recent research paper with a substantial optimization/learning component Counts for 4 HW grades

P OLICIES Office hours: W 10-11am, Info E 257 (or by appointment) Should respond to in 24 hours Late HW: 10% deducted for every day late

P REREQUISITES CS B551 or equivalent introduction to AI course. Specifically, probabilistic reasoning and Bayesian networks Calculus. (Multivariate recommended) Linear algebra

L ECTURE