Courseware : Artificial Intelligence

Abstract:: Artificial Intelligence : Course lectures, hours 42. There are 11 pdf files, total 562 pages. Topics : Introduction to AI; Problem Solving - Search and Control Strategies; Knowledge Representation Issues - Predicate Logic, Rules; Reasoning System - Symbolic, Statistical; Game Playing; Learning; Expert System; Fundamentals of Neural Networks; Fundamentals of Genetic Algorithms; Natural Language Processing; Common Sense.

Introduction to Statistical Method in Economics, Spring 2006

Abstract:: This course is a self-contained introduction to statistics with economic applications. Elements of probability theory, sampling theory, statistical estimation, regression analysis, and hypothesis testing. It uses elementary econometrics and other applications of statistical tools to economic data. It also provides a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.

Abstract:: " This course will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed for further study of econometrics and provide basic preparation for 14.32. Topics include elements of probability theory, sampling theory, statistical estimation, and hypothesis testing."

Read the Fine Print

Abstract:: In this course, the student will learn about the three laws of thermodynamics, thermodynamic principles, ideal and real gases, phases of matter, equations of state, and state changes. The student will also take a look at chemical kinetics--a branch of study concerned with the rates of reactions and other processes--as well as kinetic molecular theory and statistical mechanics, which relate the atomic-level motion of a large number of particles to the average thermodynamic behavior of the system as a whole. Upon successful completion of this course, the student will be able to: State and use laws of thermodynamics; Perform calculations with ideal and real gases; Design practical engines by using thermodynamic cycles; Predict chemical equilibrium and spontaneity of reactions by using thermodynamic principles; Describe the thermodynamic properties of ideal and real solutions; Define the phases of matter, describe phase changes, and interpret/construct phase diagrams; Relate macroscopic thermodynamic properties to microscopic states by using the principles of statistical thermodynamics; Describe reaction rates and then do calculations to determine them; Relate reaction kinetics to potential reaction mechanism; Calculate the temperature dependence of rate constants and relate that to activation energy; Describe a variety of complex reactions; Describe catalysis; Describe enzymatic catalysis. (Chemistry 105)