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Mastering Algorithms for Optimization and Learning: Neural and Genetic Approaches

This course, led by Kris Hauser in Spring 2011, explores the fundamentals of optimization and learning within the context of artificial intelligence. Participants will gain hands-on experience in defining mathematical optimization problems, understanding their characteristics, and applying various algorithms. The curriculum also covers learning objectives, including the implementation of learning algorithms and techniques such as Bayesian networks. As a blend of theory and practical application, this course equips students with the essential skills to tackle real-world challenges in AI and related fields.

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Mastering Algorithms for Optimization and Learning: Neural and Genetic Approaches

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  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

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