background preloader

Introduction to Neural Networks

Introduction to Neural Networks
CS-449: Neural Networks Fall 99 Instructor: Genevieve Orr Willamette University Lecture Notes prepared by Genevieve Orr, Nici Schraudolph, and Fred Cummins [Content][Links] Course content Summary Our goal is to introduce students to a powerful class of model, the Neural Network. We then introduce one kind of network in detail: the feedforward network trained by backpropagation of error. Lecture 1: Introduction Lecture 2: Classification Lecture 3: Optimizing Linear Networks Lecture 4: The Backprop Toolbox Lecture 5: Unsupervised Learning Lecture 6: Reinforcement Learning Lecture 7: Advanced Topics [Top] Review for Midterm: Links Tutorials: The Nervous System - a very nice introduction, many pictures Neural Java - a neural network tutorial with Java applets Web Sim - A Java neural network simulator. a book chapter describing the Backpropagation Algorithm (Postscript) A short set of pages showing how a simple backprop net learns to recognize the digits 0-9, with C code Reinforcement Learning - A Tutorial

Laboratory Fundamentals of Synthetic Biology From OpenWetWare Syllabus Class Format The Class will meet twice a week, one 2 hour classroom session, and one 3 hour lab session. Problem sets will be assigned weekly for the first eight weeks. Grades The final grade will be as follows: 20% Problem Sets 30% Midterm Exam 20% Lab Evaluations 30% Final Project Schedule Introduction - Synthetic Biology: History, current applications and future directions Powerpoint (w/content from Drew Endy) Assignments: Endy Article and Comic Strip The Biology (4 sessions) Cells, DNA, RNA and Protein DNA - information encoding, structure, sequencing and synthesis RNA - encoding, structure, function (RNA Enzymes, RNA Aptamers) Proteins - Crystallography, functions, scaffolds Introductory packet about DNA, RNA, Protein. Create a biobrick out of a sequence (force them to re-optimize a coding region into e. coli and remove a biobrick incompatibility). Reading List This work is licensed under a Creative Commons Attribution-Share Alike 3.0 License.

brains in silicon Welcome to Brains in Silicon. Learn about the lab, get to know the brains that work here, and find out about new projects that you could join. We have crafted two complementary objectives: To use existing knowledge of brain function in designing an affordable supercomputer—one that can itself serve as a tool to investigate brain function—feeding back and contributing to a fundamental, biological understanding of how the brain works. We model brains using an approach far more efficient than software simulation: We emulate the flow of ions directly with the flow of electrons—don't worry, on the outside it looks just like software. Welcome and enjoy your time here!

Synbio 2007 From OpenWetWare General Info Spring 2007 Instructor: Jay Keasling (keasling@berkeley.edu) GSI: Jeffrey Dietrich (jadietrich@gmail.com) Logistics: Lecture/Discussion: 2 hours, 10-12 AM Friday Grading: Literature Review 30% Group Project 60% Class Participation 10% Office hours: contact Jeffrey Dietrich to arrange a meeting Announcements ASSIGNMENT (Due 2/16): email Jeff with your three top choices for topics to lead in literature review group discussion. Tentative Schedule 1/19 Introduction, Basis for Synthetic Biology - Jay Keasling 1/26 Modeling and Design of Synthetic Systems - Adam Arkin Genetic models, stochastic and continuous simulations, adaption of circuit methods to SB. 2/2 Drugs from Bugs-Jay Keasling 2/9 Design of Tumor-Killing Bacteria - J. Literature Review Assignment Every student will be required to lead one class discussion over selected readings/topics assigned for that week. Group Project Ideas Group projects from 2007 (Presentations and References) a. b. c. d. Policy Approach

Related: