CS B553: Algorithms for Optimization and Learning

1. CS B553: Algorithms for Optimization and Learning aka “Neural and Genetic Approaches to Artificial Intelligence” Spring 2011Kris Hauser

2. Today’s Agenda • Topics covered • Prerequisites • Class organization & policies • Coursework • Math review

3. What is Optimization? • 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

4. Optimization Learning Objectives • 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

5. What is Learning? • 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!

6. Learning Learning Objectives • 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

7. Organization • http://www.cs.indiana.edu/classes/b553-hauserk • Lectures, readings • Lecture notes for optimization unit • Probabilistic Graphical Models: Principles and Techniques (Koller and Friedman)for learning unit • In-class group exercises

8. Course 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

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

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

11. Lecture