Below, for each problem on this Midterm Exam, Perfect is the percentage of students who received
full credit, Partial is the percentage who received partial credit, and Zero is the percentage who
received zero credit.
(Due to rounding or other exceptional
CS-171, Intro to A.I. Mid-term Exam Summer Quarter, 2016
YOUR NAME:
YOUR ID:
ID TO RIGHT:
ROW:
SEAT:
The exam will begin on the next page. Please, do not turn the page until told.
When you are told to begin the exam, please check first to make sure that y
Below, for each problem on this Midterm Exam, Perfect is the percentage of students who received
full credit, Partial is the percentage who received partial credit, and Zero is the percentage who
received zero credit.
(Due to rounding, values below may be
For the Final Exam, Perfect gives the percentage of students who received full credit, Partial gives the
percentage who received partial credit, and Zero gives the percentage who received zero credit.
(Due to rounding, etc., values below may be only appro
CS-171, Intro to A.I. Mid-term Exam Winter Quarter, 2016
YOUR NAME:
YOUR ID:
ID TO RIGHT:
ROW:
SEAT:
The exam will begin on the next page. Please, do not turn the page until told.
When you are told to begin the exam, please check first to make sure that y
For each problem on this test, below Perfect gives the percentage who
received full credit, Partial gives the percentage who received partial credit,
and Zero gives the percentage of students who received zero credit.
(Due to rounding, values below may be
For each question on Quiz #4, Zero gives the percentage of students who
received zero, Partial gives the percentage who received partial credit, and Full
gives the percentage who received 100%. (Due to rounding, numbers shown below
are only an approximate
Below, for each problem on this test, Perfect is the percentage of students who
received full credit, Partial is the percentage who received partial credit, and Zero
is the percentage who received zero credit.
(Due to rounding, values below may be only ap
CS-171, Intro to A.I., Summer Quarter, 2016 Quiz # 2 20 minutes
NAME:
YOUR ID:_ ID TO RIGHT:_ ROW:_ SEAT:_
1. (48 pts total, 3 pts each) Execute Uniform Cost Search using Tree Search (i.e., do not
remember visited nodes). S is the Start node, and G is the
For each problem on this test, below Perfect gives the percentage who received
full credit, Partial gives the percentage who received partial credit, and Zero
gives the percentage of students who received zero credit.
(Due to rounding, values below may be
CS-171, Intro to A.I., Summer Quarter, 2016 Quiz # 3 20 minutes
NAME:
YOUR ID:_ ID TO RIGHT:_ ROW:_ SEAT:_
1. (60 pts total, 12 pts each) STATE-SPACE SEARCH. Execute Tree Search through this graph (do not remember
visited nodes, so repeated nodes are poss
Below, for each problem on this test, Perfect is the percentage of students who
received full credit, Partial is the percentage who received partial credit, and Zero
is the percentage who received zero credit.
(Due to rounding, values below may be only ap
CS-171, Intro to A.I. Quiz#4 Summer Quarter, 2016 20 minutes
YOUR NAME AND EMAIL ADDRESS:
YOUR ID:
ID TO RIGHT:
ROW:
SEAT:
1. (25 pts total, -5 pts for each error, but not negative) MINI-MAX SEARCH IN GAME TREES.
The game tree below illustrates a position
HW1
CS 171 Intro to AI
Winter 2017
Due at EEE class dropbox: Thursday Jan 19, 11:59PM
(20 points each)
1.
Exercise 1.1 (R&N 3rd ed., pg. 31)
2.
Exercise 1.2 (R&N 3rd ed., pg. 31) See EEE web site for the paper,
turing1950.pdf
3.
Exercise 2.4 (R&N 3rd ed.,
CS 339: Computer Network
Assignment 4
Due on Friday, May 10, 2013
Instructor:Liping Shen
Name:Wanchao Liang
ID:5100309610
Email:lwcallenhome@gmail.com
Problem 4
Consider the network below:
a. Suppose that this network is a datagram network. Show the forwa
The Importance of
A Good Representation
You cant learn what
you cant represent.
- G. Sussman
Properties of a good representation:
Reveals important features
Hides irrelevant detail
Exposes useful constraints
Makes frequent operations easy-to-do
Supports l
Informed search algorithms
This lecture topic
Read Chapter 3.5-3.7
Next lecture topic
Read Chapter 4.1-4.2
(Please read lecture topic material before
and after each lecture on that topic)
You will be expected to know
evaluation function f(n) and heuristic
Local Search Algorithms
This lecture topic
Read Chapter 4.1-4.2
Next lecture topic
Read Chapter 5
(Please read lecture topic material before and
after each lecture on that topic)
You will be expected to know
Local Search Algorithms
Hill-climbing search
Uninformed (also called blind)
search algorithms
This Lecture
Read Chapter 3.1-3.4
Next Lecture
Read Chapter 3.5-3.7
(Please read lecture topic material before and after each lecture on that topic)
You will be expected to know
Overview of uninformed sear
Solving problems by
searching
This Lecture
Read Chapters 3.1 to 3.4
Next Lecture
Read Chapter 3.5 to 3.7
(Please read lecture topic material before and after each lecture on that topic)
1
You will be expected to know
State-space search
Definitions of a
Introduction to AI
&
Intelligent Agents
This Lecture
Read Chapters 1 and 2
Next Lecture
Read Chapter 3.1 to 3.4
(Please read lecture topic material before and after each lecture on that
topic)
You will be expected to know
Agent: Anything that can be viewe
Handout 1
EE124: Introduction to Neuroelectrical Engineering
EE124, Shenoy
What is Neuroelectrical Engineering?
The field is extremely young, vibrant, growing and is still being defined.
So dont think that a clear definition really even exists yet!
But
Lecture 5: Passive Electrical Properties
Reading assignment from Kandell, Schwartz & Jessell:
Chapter 8 Local Signaling: Passive Electrical Properties
of the Neuron
We are now equipped to calculate Vm for any set of:
ionic concentration gradients and
Lecture 6: Propagated Signals Action Potentials
Reading assignment from Kandell, Schwartz & Jessell:
Chapter 9 Propagated Signaling: The Action Potential
Neurons can carry information long distances b/c of action potentials.
Action potentials (APs or
Lecture 3: Intro Continued & Ion Channels
Reading assignment from Kandell, Schwartz & Jessell:
Chapter 6 Ion Channels
Lecture 2 left off having introduced neurons and action potentials.
Lecture 3 will complete our introduction to the nervous system.
Outline
Outline
Outline might be subject to minor modifications and updates.
Architecture:
Topics: circuit switching, telephone topology, Packet switching, Internet topology, cellular
topology, cable topology, Technology convergence, packet switching, del
1
Homework 1 - 2015
Due date: Thursday 10/22/2015.
Points pre-assigned to problems are just an indication of the nal score: partial and unsatisfactory solutions
will receive a fraction of the points, whereas excellent answers may exceed the indicated poin