Biology 141, Spring 2012
Exam 1 Study Questions
(rev. 022112)
The questions on the first hour exam will be based on those given below. Questions on
the exam will be in multiple choice form, but if you can answer the questions here, you
will have no troubl
EXAM 2 STUDY QUESTIONS
(10/7/12)
The questions on the second hour exam will be based on those given below. If you can answer
the questions here, you should have little trouble with the exam. [Note: These questions are
meant to provoke thought and study. A
function [J, grad] = costFunctionReg(theta, X, y, lambda)
%COSTFUNCTIONREG Compute cost and gradient for logistic regression with
regularization
%
J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
%
theta as the parameter for regularized
0
Name: Class: Date:
BIOL 142 Exam 2
ID'B
Min,
Multiple Choice
Identify the choice that best completes the statement or answers the question.
Two species of birds with somewhat diﬁ‘erent coloration live on opposite sides of a peninsula. _Qne
species has a
Factors affecting population and geographic distribution of a Species
The distribution of most species is confined to specific geographical areas. Understanding the form that
geographic range limits take, their causes and their consequences are key goals
Exponential and Logistic Population Growth
In Lake Malawi, located in eastern Africa, approximately 500-1000 species of
cichlid fish have evolved from a single ancestor in the last 1-2 million years, one
of the most rapid diversifications known to science
Tree Thinking
In the age of genomics, groups of organisms can be compared by their DNA and evolutionary relationships can be inferred based on information
in these sequences. There are a many different algorithms in bioinformatics used to build molecular
Module 12 Worksheet: Biodiversity
Biodiversity is a term used to describe the diversity of life and living systems and
can measured at different hierarchical levels. In this module we will focus on
measuring and interpreting species diversity in communiti
Name _
Section _
INVESTIGATING HOMINID EVOLUTION USING SKULL REPLICAS
Introduction:
In this active learning exercise, you will make detailed observations and measurements of skull replicas of nine
modern and extinct hominids. You will then use your data t
Name:_
Worksheet: Ecological interactions and evolution: Frequency dependent
selection
Ecological interactions among individuals (e.g., territorial defense, predator-prey
relationships) strongly influence fitness, and so the traits that influence the
outc
Selection on Migrating and Drifting Bunnies: The Effect of Gene Flow and Genetic
Drift on the Efficiency of Natural Selection in Evolution
Biological evolution is a change in the frequency of alleles in a population over a period
of time. The type of evol
Modeling Density Independent Population Growth
An important question in population ecology is to understand the influence of
habitat on population growth rates. Rates of population growth often reflect the
degree to which a species (or population) is adap
Species Concepts
Species are often considered to be the basic category of Morphologicalclassification.
However, there are at least 25 different species concepts used to define a species.
Different species concepts may be have important implications for ho
Natural Selection in Galapagos Finches
Question: Did natural selection on ground finches occur when the environment changed?
Introduction
In 1973, biologists Peter and Rosemary Grant began a 30-year long study to find out if they could document
natural se
Sneha Saggurthi
2/26/15
Bio Questions #32-37
32. Textbooks often refer to oxygen as the "final electron acceptor." What does this statement mean?
32.1 Oxygen is the terminal electron acceptor because of its highly positive reduction potential,
therefore,
function p = predict(theta, X)
%PREDICT Predict whether the label is 0 or 1 using learned logistic
%regression parameters theta
%
p = PREDICT(theta, X) computes the predictions for X using a
%
threshold at 0.5 (i.e., if sigmoid(theta'*x) >= 0.5, predict 1
EXAM 3 STUDY QUESTIONS
(12/02/12)
The questions on the third hour exam will be based on those given below. If you can answer the
questions here, you should have little trouble with the exam. [Note: These questions are meant
to provoke thought and study. A
function p = predictOneVsAll(all_theta, X)
%PREDICT Predict the label for a trained one-vs-all classifier. The labels
%are in the range 1.K, where K = size(all_theta, 1).
% p = PREDICTONEVSALL(all_theta, X) will return a vector of predictions
% for each e
% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all
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Instructions
-This file contains code that helps you get started on the
linear exercise. You will need to complete the following functions
in this exericse:
lr
function p = predict(Theta1, Theta2, X)
%PREDICT Predict the label of an input given a trained neural network
%
p = PREDICT(Theta1, Theta2, X) outputs the predicted label of X given the
%
trained weights of a neural network (Theta1, Theta2)
% Useful value
function [J, grad] = costFunction(theta, X, y)
%COSTFUNCTION Compute cost and gradient for logistic regression
%
J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
%
parameter for logistic regression and the gradient of the cost
%
w.r.t
function plotData(X, y)
%PLOTDATA Plots the data points X and y into a new figure
%
PLOTDATA(x,y) plots the data points with + for the positive examples
%
and o for the negative examples. X is assumed to be a Mx2 matrix.
% Create New Figure
figure; hold o
function plotDecisionBoundary(theta, X, y)
%PLOTDECISIONBOUNDARY Plots the data points X and y into a new figure with
%the decision boundary defined by theta
%
PLOTDECISIONBOUNDARY(theta, X,y) plots the data points with + for the
%
positive examples and o
function [all_theta] = oneVsAll(X, y, num_labels, lambda)
%ONEVSALL trains multiple logistic regression classifiers and returns all
%the classifiers in a matrix all_theta, where the i-th row of all_theta
%corresponds to the classifier for label i
%
[all_t
function [h, display_array] = displayData(X, example_width)
%DISPLAYDATA Display 2D data in a nice grid
%
[h, display_array] = DISPLAYDATA(X, example_width) displays 2D data
%
stored in X in a nice grid. It returns the figure handle h and the
%
displayed
function [J, grad] = lrCostFunction(theta, X, y, lambda)
%LRCOSTFUNCTION Compute cost and gradient for logistic regression with
%regularization
%
J = LRCOSTFUNCTION(theta, X, y, lambda) computes the cost of using
%
theta as the parameter for regularized l
function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
% Minimize a continuous differentialble multivariate function. Starting point
% is given by "X" (D by 1), and the function named in the string "f", must
% return a function value and a vector
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Machine Learning Online Class - Exercise 2: Logistic Regression
Instructions
-This file contains code that helps you get started on the logistic
regression exercise. You will need to complete the following functions
in th