Fall 2007

First day of classes for CMU & Pitt: Monday, August 27, 2007.

CNBC Core courses:  Cognitive Neuroscience,
Computational Models of Neural Systems,
Cellular & Molecular Neurobiology

CMU Biomedical Engineering

42-709 Special Topics: Neuroimaging: 12 Units
(Cross listed as PITT BIOE 2600)

• Instructor: Dr. Pravat Mandal
  Co-Instructor: Dr. Jelena Kovacevic
• Location: Benedum Hall 720
• Days/Times: Tue/Fri 9:00AM to 10:25AM

This course consists of six state-of-the-art imaging techniques (I.e., mri, mrs, fmri, pet, meg/eeg and optical). Each part of the module will present in-depth analysis of the particular technique and its application in neuroscience research. Apart from in-depth presentation of each technique, students will also get acquainted with the operation of the respective instruments. Tour to that respective imaging facility will be guided by the concerned faculty and the scientific staff member in that respective facility will assist for demonstration.

CMU Computer Science — Classes will not begin until September 10, 2007 — after the CSD Ph.D. Immigration Course

15-780 Graduate Artificial Intelligence: 12 Units

• Instructors: Ziv Bar-Joseph & Geoffrey Gordon
• Location: Wean Hall 5409
• Days/Times: TR 10:30AM to 11:50AM

This course is targeted at graduate students who need to learn about current-day research, and about how to perform current-day research, in Artificial Intelligence---the discipline of designing intelligent decision-making machines. Techniques from Probability, Statistics, Economics, Algorithms, Operations Research and Optimal Control are increasingly important tools for improving the intelligence and autonomy of machines, whether those machines are robots surveying Antarctica, schedulers moving billions of dollars of inventory, spacecraft deciding which experiments to perform, or vehicles negotiating for lanes on the freeway. This AI course is a review of a selected set of these tools. The course will cover the ideas underlying these tools, their implementation, and how to use them or extend them in your research.

15-781 Artificial Intelligence: Machine Learning: 12 Units

• Instructor: Carlos Guestrin
• Location: Margaret Morrison A14
• Days/Times: MW 3:00PM to 4:20PM

It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in learning and data mining or who may need to apply learning or data mining techniques to a target problem. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statistics and from statistical algorithmics. Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.

15-883 Computational Models of Neural Systems: 12 units [CNBC core course]

• Instructor: David Touretzky
• Location: Wean Hall 4615A
• Days/Times: MW 4:30PM to 5:50PM

This course is an in-depth study of information processing in real neural systems from a computer science perspective. We will examine several brain areas where processing is sufficiently well understood that it can be discussed in terms of specific representations and algorithms. We will focus primarily on computer models of these systems, after establishing the necessary anatomical, physiological, and psychophysical context. There will be some neuroscience tutorial lectures for those with no prior background in this area.

CMU Machine Learning

10-701 Machine Learning: 12 units
(Cross-listed as 15-781 for CS PhD students only.)

• Instructors: Tom Mitchell & Andrew Moore
• Location: Wean Hall 7500
• Days/Times: TR 10:30AM to 11:50AM

It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in learning and data mining or who may need to apply learning or data mining techniques to a target problem. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statistics and from statistical algorithmics. Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.
Please refer to this link for the most recent schedule updates.

This course is only available to CSD PhD and 5th year MS students. If you are from another department you must register under the number 10-701 (MLD). If you have questions send email to diane@cs

CMU Psychology

The purpose of this course is to enable students to develop a solid understanding of major methodological issues in the study of psychology. The focus will be on issues and techniques that are especially applicable to cognitive, developmental, social, and neuroscience areas, though many of the issues apply to all areas within the field.

Graduate Students Only.

Languages may be the most complex systems people ever master, and yet infants appear to learn them effortlessly. By contrast, adults often struggle to acquire language. This class will explore theoretical controversies and experimental results in an attempt to understand how infants acquire language, and the way that acquisition might differ between infancy and adulthood. Throughout the course, there will be a focus on the potential role of learning in language acquisition, the strengths and limitations of the experimental methods that are appropriate for use with infants, and the relation between theoretical constructs and experimental results. The course will be reading intensive, and evaluation will be based upon both written assignments and oral participation.

Special Permission Required: Please email Dr. Erik Thiessen at thiesen@andrew.cmu.edu for instructors permission for graduate course. Erin Donahoe in BH 342E can register you.

85-765 Cognitive Neuroscience [CNBC core course]
Cross-listed as Pitt Neuroscience NROSCI 2005.

• Instructor: Carl Olson : 9 units
• Location: Mellon Institute 335
• Days/Times: TR 10:30AM to 11:50AM

  This course will cover fundamental findings and approaches in cognitive neuroscience, with the goal of providing an overview of the field at an advanced level. Topics will include high-level vison, spatial cognition, working memory, long-term memory, learning, language, executive control, and emotion. Each topic will be approached from a variety of methodological directions, for example, computational modeling, cognitive assessment in brain-damaged humans, non-invasive brain monitoring in humans, and single-neuron recording in animals. Lectures will alternate with sessions in seminar format. Prerequisites: Graduate standing or two upper-level psychology courses from the areas of developmental psychology, cognitive psychology, computational modeling of intelligence, neuropsychology or neuroscience. .
  
  Special permission is required: Graduate Students, instructors permission from Carl Olson at colson@cmu.edu and once you have instructor's permission, please see Erin Donahoe , in BH 342 E or donahoe@andrew.cmu.edu to register you.

Perception, broadly defined, is the construction of a representation of the external world for purposes of thinking and acting. Although we often think of perception as the processing of inputs to the sense organs, the world conveyed by the senses is ambiguous, and cognitive and sensory systems interact to interpret it. In this course, we will examine the sensory-level mechanisms involved in perception by various sensory modalities, including vision, audition, and touch. We will learn how sensory coding interacts with top-down processing based on context and prior knowledge and how perception changes with learning and development. We will look at methods of psychophysics, neuroscience, and cognitive psychology. The goals include not only imparting basic knowledge about perception but also providing new insights into everyday experiences.

85-803 Evaluation of Computational Cognitive Architecturese: 9 units

CMU Robotics

16-720 Section A, Computer Vision: 12 units

• Instructor: Martial Herbert
• Hamburg Hall B103
• Days/Times: TR 1:30PM - 2:50PM

This course deals with the science and engineering of computer vision, that is, the analysis of patterns in visual images of the world with the goal of reconstructing and understanding the objects and processes in the world that are producing them. The emphasis is on physical, mathematical, and information processing aspects of vision. Topics covered include image formation and representation, camera geometry and calibration, multi-scale analysis, segmentation, contour and region analysis, energy-based techniques, reconstruction of based on stereo, shading and motion, 3-D surface representation and projection, and analysis and recognition of objects and scenes using statistical and model-based techniques. The material is based on a recent graduate-level textbook augmented with research papers, as appropriate. The course involves considerable Matlab programming exercises.

Textbook Information:
Title: "Computer Vision: A Modern Approach"
Authors: David Forsyth and Jean Ponce
Publisher: Prentice Hall
ISBN: 0-13-085198-1

 CMU Statistics

36-749 Experimental Design for Behavioral and Social Sciences: 12 units
Cross-listed as 36-309

• Instructor: Howard Seltman
• Location: Lecture - Porter Hall 100, Sections A, B, C, D - Baker Hall 140 C&F
• Days/Times: Lecture TR.12:00PM to 1:20PM. Section A: R/12:00PM to 1:20PM, Section B: R/1:30PM to 2:50PM, Section C: F/12:00PM to 1:20PM and Section D F/1:30 to 2:50 PM

  Statistical aspects of the design and analysis of planned experiments are studied in this course. A clear statement of the experimental factors will be emphasized. The design aspect will concentrate on choice of models, sample size and order of experimentation. The analysis phase will cover data collection and computation, especially analysis of variance, and will stress the interpretation of results. In addition to weekly lecture, students will attend a computer lab once a week. Prerequisite: 36-202, 36-220, or 36-247

Pitt Bioengineering 

BIOE 2600 Neuroimaging CR HRS: 3.0
(Cross listed as CMU 42-709)

• Instructor: Dr. Pravat Mandal
  Co-Instructor: Dr. Jelena Kovacevic
• Location: Benedum Hall 720
• Days/Times: Tue/Fri 9:00AM to 10:25AM

This course consists of six state-of-the-art imaging techniques (i.e., MRI, MRS, fMRI, PET, MEG/EEG and Optical). Each part of the module will present indepth analysis of the each technique and its application in neuroscience research. Apart from in-depth presentation of the each technique, students will also get acquainted with the operation of the respective instruments. Tour to that respective facility will be guided by the concerned faculty and scientific staff member in that respective facility will assist for demonstration. This course is a joint program between PITT and CMU.

References:

• The Essential Physics of Medical Imaging (J. T. Bushberg et al.)

• Magnetic Resonance Spectroscopy and its application in Alzheimer's disease. Concepts in Magnetic Resonance, Pravat K Mandal, Vol 30A 1-25 (2007)

• MRI in practice (Westbrook, Roth and Talbot)

Pitt Mathematics
 

NONE

Pitt Neuroscience

NROSCI 2005 Cognitive Neuroscience CR HRS: 3.0 [CNBC core course]
Cross-listed as CMU 85-765

• Instructor: Carl Olson : 9 units
• Location: Mellon Institute 335
• Days/Times: TR 10:30AM to 11:50AM

  This course will cover fundamental findings and approaches in cognitive neuroscience, with the goal of providing an overview of the field at an advanced level. Topics will include high-level vision, spatial cognition, working memory, long-term memory, learning, language, executive control, and emotion. Each topic will be approached from a variety of methodological directions, i.e. computational modeling, cognitive assessment in brain-damaged humans, non-invasive brain monitoring in humans and single-neuron recording in animals. Lecture format will be used for most sessions, with a few sessions devoted to discussion.

Prerequisites: Graduate standing or permission of the instructor.

 

NROSCI 2011 Functional Neuroanatomy: CR HRS: 4.0

• Instructor: Susan Sesack
• Location:  Langley 0A221
• Days/Times: M/F 9:00 AM to 9:50 AM; W 8:00 AM to 9:50 AM
• Note: This class is full; no new registrations can be accepted.

This course covers the basic structure of the central nervous system from spinal cord to cerebral cortex. The major sensory, motor and integrative neural systems of the human brain are discussed. Based on an understanding of normal neural connections and brain function, the anatomical and physiological basis of various neurological disorders of the nervous system will be explored. Special Enrollment Counseling is required for registration. Students should contact Dr. Sesack for permission to register.

Prerequisites: NROSCI 1000 or 1003. Special Enrollment Counseling is required for registration. Students should contact Dr. Sesack for permission to register.

NROSCI/MSNBIO 2100 Cellular and Molecular Neurobiology 1: CR HRS: 4.0 [CNBC core course]
NROSCI/MSNBIO 2101 Cellular and Molecular Neurobiology 2: CR HRS: 4.0 [CNBC core course]

• Instructor: Carl Lagenaur
• Location: Victoria Hall room 111 (MWHF) and room 230 (T)
• Days/Time: MTWHF 10:00A to 11:50A
• Note: CNBC students must take both 2100 and 2101; the two parts are taught sequentially.

2100 - This course is the first component of the introductory graduate sequence designed to provide an overview of cellular and molecular aspects of neuroscience. This course covers nerve cell biology, protein chemistry, regulation of gene expression, receptor function, and second messenger signaling in a lecture format. A conference designed to develop critical reading skills will cover primary literature corresponding to material covered in each block. Students will be expected to read and discuss original scientific literature.

2101 - This course is the second component of the introductory graduate sequence designed to provide an overview of cellular and molecular aspects of neuroscience. This course covers the electrical properties of neurons, synaptic transmission and neural development.

Prerequisites: A background in basic biology and permission of the instructor is required.

Note for CMU students: Section 2 of the PCHE Cross Registration Request Form provides a space for students to enroll in a primary choice (course), and a secondary choice in case the primary is not available. Please register for the NROSCI sections as your primary chioce and the MSNBIO sections as your secondary choice, so that when NROSCI fills up, the Registrar's Office will automatically put you in the MSNBIO section without having to complete any additional paperwork.

Note for non-Neuroscience students: The 2100/2101 sequence assumes a substantial background in biology. Students who lack this background and cannot devote substantial time to background reading might prefer to take the graduate section of Jon Johnson's undergraduate neurophysiology course, NROSCI 2012, in the Spring instead.

Pitt Psychology

PSY 2005 Statistical Analysis I / Advanced Statistics-UG: CR HRS: 3.0

• Instructors: Jeewon Cheong
• Location: SENSQ 4125
• Days/Times: M 1:00 pm to 3:50 pm

This course is the first of a two course sequence to provide the knowledge and skills needed to plan and conduct analyses using a uniform framework based on the general linear model. Students will learn techniques to conduct a variety of statistical tests; the appropriate interpretation of results will be emphasized. Topics include descriptive statistics, graphing data, sampling distributions, hypothesis testing (including power, effect sizes, and confidence intervals), T-tests, correlations, multiple regression, and polynomial regression. Students use SAS for statistical computations.

 

PSY 2205 Psychopathology: CR HRS: 3.0

• Instructor: Michael Pogue-Geile
• Location:  SENSQ 4125
• Days/Times: TR 3:00P to 4:15P

This graduate course provides a critical background in research strategies, phenomena, empirical research, and models of adult psychopathology. The course emphasis will be on etiological and pathological research, with both psychological and biological findings to be discussed. Course concentration will be on the major psychopathologies with clinical onset in adulthood, including schizophrenia, affective disorders, anxiety, addictions, and eating disorders. Conceptual and methodological issues that cross diagnostic categories will be stressed. Treatment approaches and differential diagnosis will be covered but not emphasized.

Prerequisites: Permission of the instructor if not psychology graduate student.

PSY 2455 Human Cognition: Language: CR HRS: 3.0

• Instructor: Charles Perfetti
• Location: LDRC
• Days/Times: R 9:30AM to 11:55AM

This is a basic half-term module in language processes. It offers intensive coverage of the basic (adult) psycholinguistic topics such as speech perception, word meaning, and sentence comprehension.

PSY 2475 Cognitive Neuroscience: CR HRS: 3.0

• Instructor: Julie Fiez
• Location: LDRC
• Days/Times: W 2:00PM to 4:25PM

This course is designed to fulfill a core requirement for doctoral students in the clinical and cognitive training programs within the psychology department. The objective of the course is to introduce psychology graduate students with little or no prior exposure to neuroscience to the facts and methodologies of systems and cognitive neuroscience, neurophysiology, neuropharmacology, and cellular and molecular neurobiology. This is accomplished through both lectures and paper discussions. The lectures introduce the basic facts and methods of neuroscience, and they provide an overview of what is known from neuroscience about general topics of interest within psychology. The readings probe a more restricted topic in greater depth, and their interpretation requires students to apply their growing knowledge of neuroscience facts and methods.

PSY 2476 Topics in Cognitive Psychology: Functional MRI: CR HRS: 3.0

• Instructor: Mark Wheeler, Ph.D.
• Location: LRDC, room TBA
• Days/Times: T/Th 10:00 AM - 11:15 AM

An introduction to MRI methods, design, and analysis related to cognitive and behavioral functional MRI. This course will cover basic principles of MR signal formation and measurement, physiological mechanisms underlying BOLD fMRI signal, pulse sequences, signal and noise, functional imaging techniques, experimental design, and basic statistical analysis. This course is open to graduate students at 3 credits. Advanced undergraduate students may enroll with permission of instructor. Other students may enroll at 2-3 credits.