Fall 2005
ECE 570 - Artificial Intelligence
Prof. Jeffrey Siskind
Credits: 3.0, Enrolled: Fall 2005, Grade: A
Introduction to the basic concepts and various approaches of artificial intelligence. The first part of the course deals with heuristic search and shows how problems involving search can be solved more efficiently by the use of heuristics and how, in some cases, it is possible to discover heuristics automatically. The next part of the course presents ways to represent knowledge about the world and how to reason logically with that knowledge. The third part of the course introduces the student to advanced topics of AI drawn from machine learning, natural language understanding, computer vision, and reasoning under uncertainty. The emphasis of this part is to illustrate that representation and search are fundamental issues in all aspects of artificial intelligence.
ECE 608 - Computational Models And Methods
Prof. Mithuna Thottethodi
Credits: 3.0, Enrolled: Fall 2005, Grade: A
Computation models and techniques for the analysis of algorithm complexity. The design and complexity analysis of recursive and nonrecursive algorithms for searching, sorting, set operations, graph algorithms, matrix multiplication, polynomial evaluation and FFT calculations. NP-complete problems.
ECE 632 - Machine Learning And Data Mining
Prof. Bob Givan
Credits: 3.0, Enrolled: Fall 2005, Grade: A
Machine learning is concerned with computer programs that automatically improve their performance through experience. Knowledge discovery in databases is concerned with extracting useful patterns or deviations from data using "data mining" methods. This course introduces students to the primary approaches to machine learning and data mining from a variety of fields, including inductive inference of decision trees, neural network learning, statistical learning methods, reinforcement learning, clustering, and discovery. In addition, this course introduces theoretical concepts, such as inductive bias and the PAC (Probably Approximately Correct) learning framework.
Spring 2006
ECE 580 - Optimization Methods For Systems And Control
Prof. Stanislaw Zak
Credits: 3.0, Enrolled: Spring 2006, Grade: B
Introduction to optimization theory and methods, with applications in systems and control. Nonlinear unconstrained optimization, linear programming, nonlinear constrained optimization, various algorithms and search methods for optimization, and their analysis. Examples from various engineering applications are given.
ECE 600 - Random Variables And Signals
Prof. Mary Comer
Credits: 3.0, Enrolled: Spring 2006, Grade: B
Engineering applications of probability theory. Problems on events, independence, random variables, distribution and density functions, expectations, and characteristic functions. Dependence, correlation, and regression; multivariate Gaussian distribution. Stochastic processes, stationarity, ergodicity, correlation functions, spectral densities, random inputs to linear systems; Gaussian processes.
ECE 662 - Pattern Recognition And Decision-Making Processes
Prof. Mimi Boutin
Credits: 3.0, Enrolled: Spring 2006, Grade: A
Introduction to the basic concepts and various approaches of pattern recognition and decision-making processes. The topics include various classifier designs, evaluation of classifiability, learning machines, feature extraction and modeling.
STAT 598G - Statistical Machine Learning
Prof. Guy Lebanon
Credits: 1.0, Enrolled: Spring 2006, Grade: A
This group meets weekly to discuss original and non-original research in machine learning and related fields. The participants are encouraged to read the assigned readings before the meeting. Currently, the group participants are faculty and graduate students from Statistics, Electrical and Computer Engineering, Computer Science, Industrial Engineering and Civil Engineering.
Fall 2006
STAT 528 - Introduction To Mathematical Statistics
Prof. Jayanta Ghosh
Credits: 3.0, Enrolled: Fall 2006, Grade: B
Distribution of mean and s2 in normal samples, sampling distributions derived from the normal distribution, Chi square, t and F. Distribution of statistics based on ordered samples. Asymptotic sampling distributions. Introduction to multivariate normal distribution and linear models. Sufficient statistics, maximum likelihood, least squares, linear estimation, other methods of point estimation, and discussion of their properties, Cramer-Rao inequality and Rao-Blackwell theorem. Tests of statistical hypotheses, simple and composite hypotheses, likelihood ratio tests, power of tests.
STAT 598N - Statistical Machine Learning
Prof. Guy Lebanon
Credits: 1.0, Enrolled: Fall 2006, Grade: A
This group meets weekly to discuss original and non-original research in machine learning and related fields. The participants are encouraged to read the assigned readings before the meeting. Currently, the group participants are faculty and graduate students from Statistics, Electrical and Computer Engineering, Computer Science, Industrial Engineering and Civil Engineering.
Spring 2007
STAT 695N - Topics in Machine Learning: Information Theory, Statistics, and Learning
Prof. Guy Lebanon
Credits: 3.0, Enrolled: Spring 2007, Grade: A
This course will start with a basic introduction to information theory. It will consist of the fundamental concepts of entropy, mutual information and KL divergence, and the major results in data compression and communication through a noisy channel. Then, we will move to cover more advanced topics in classical information theory such as rate distortion theory, differential entropy and optimal gambling. We will conclude the course with modern research topics such as maximum entropy models, large deviations and approximate inference in graphical models. The course will have a strong theoretical component, but will also focus on applications and computing.
CS 590N - Statistical Relational Learning
Prof. Jennifer Neville
Credits: 3.0, Enrolled: Spring 2007, Grade: A
This course will provide an introduction to recent research in statistical relational learning. The course will survey recent approaches that combine probabilistic and logical representations to model relational and network datasets, focusing on fundamental challenges in representation, learning, and inference. We will review conventional graphical models and inductive logic programming approaches as needed for background. [link]
Fall 2007
ECE 664 - Computability, Complexity, and Formal Languages
Prof. Avi Kak
Credits: 3.0, Enrolled: Fall 2007, Grade: A
Topics in computability theory and formal languages include recursive function theory, the equivalence of various generic programming languages for numeric calculations and string manipulations, regular languages and finite state automata, and context-free and context-sensitive languages. In complexity theory, emphasis is on the theory of NP-completeness, including proof methods, the distinctions between strong- and weak-sense NP-completeness, NP-hardness, and performance-guaranteed approximation algorithms. [link]
Spring 2008
STAT 532 - Elements Of Stochastic Processes
Prof. Burgess Davis
Credits: 3.0, Enrolled: Spring 2008, Grade: B
A basic course in stochastic models, including discrete and continuous time Markov chains and Brownian motion, as well as an introduction to topics such as Gaussian processes, queues, epidemic models, branching processes, renewal processes, replacement, and reliability problems.
STAT 598M - Wavelets and Multiresolution Analysis
Prof. Guy Lebanon
Credits: 1.0, Enrolled: spring 2008, Grade: A
The course will cover the fundamentals of wavelets and multiresolution analysis with an emphasis on their applications to statistics and machine learning. We will start by reviewing Fourier analysis and then proceed to present various topics related to wavelets. We will follow some of the sections in the textbook "A Wavelet Tour of Signal Processing" by S. Mallat (2nd edition) as well as some papers describing machine learning and statistics applications. The course will have a substantial mathematical component but there is no official prerequisite beyond mathematical maturity and a solid background in calculus. [link]
Fall 2008 (tentative)
ECE 641 - Digital Image Processing II
Prof. Charles Bouman
Credits: 3.0, Planned: Fall 2008, Grade: -
An advanced treatment of selected topics in digital image processing. Image models, color, digital video, synthetic aperture radar, magnetic resonance imaging, stack filters, morphological filters, in-verse problems in computational vision, multiscale techniques.[link]