CSci 127: Introduction to Computer Science
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1 CSci 127: Introduction to Computer Science hunter.cuny.edu/csci CSci 127 (Hunter) Lecture 9 7 November / 41
2 Announcements Each lecture includes a survey of computing research and tech in NYC. Today: Prof. Susan Epstein Machine Learning CSci 127 (Hunter) Lecture 9 7 November / 41
3 Today s Topics Recap: Functions & Top Down Design Mapping GIS Data Loops CS Survey CSci 127 (Hunter) Lecture 9 7 November / 41
4 In Pairs or Triples: What are the formal parameters for the functions? What is the output of: r = prob4(4,"city") print("return: ", r) What is the output of: r = prob4(2,"university") print("return: ", r) CSci 127 (Hunter) Lecture 9 7 November / 41
5 Python Tutor (Demo with pythontutor) CSci 127 (Hunter) Lecture 9 7 November / 41
6 In Pairs or Triples: Write the missing functions for the program: def main(): tess = setup() #Returns a purple turtle with pen up. for i in range(5): x,y = getinput() #Asks user for two numbers. marklocation(tess,x,y) #Move tess to (x,y) and stamp. CSci 127 (Hunter) Lecture 9 7 November / 41
7 Group Work: Fill in Missing Pieces def main(): tess = setup() #Returns a purple turtle with pen up. for i in range(5): x,y = getinput() #Asks user for two numbers. marklocation(tess,x,y) #Move tess to (x,y) and stamp. CSci 127 (Hunter) Lecture 9 7 November / 41
8 Group Work: Fill in Missing Pieces 1 Write import statements. import turtle def main(): tess = setup() #Returns a purple turtle with pen up. for i in range(5): x,y = getinput() #Asks user for two numbers. marklocation(tess,x,y) #Move tess to (x,y) and stamp. CSci 127 (Hunter) Lecture 9 7 November / 41
9 Third Part: Fill in Missing Pieces 1 Write import statements. 2 Write down new function names and inputs. import turtle def setup(): #FILL IN def getinput(): #FILL IN def marklocation(t,x,y): #FILL IN def main(): tess = setup() #Returns a purple turtle with pen up. for i in range(5): x,y = getinput() #Asks user for two numbers. marklocation(tess,x,y) #Move tess to (x,y) and stamp. CSci 127 (Hunter) Lecture 9 7 November / 41
10 Third Part: Fill in Missing Pieces 1 Write import statements. 2 Write down new function names and inputs. 3 Fill in return values. import turtle def setup(): #FILL IN return(newturtle) def getinput(): #FILL IN return(x,y) def marklocation(t,x,y): #FILL IN def main(): tess = setup() #Returns a purple turtle with pen up. for i in range(5): x,y = getinput() #Asks user for two numbers. marklocation(tess,x,y) #Move tess to (x,y) and stamp. CSci 127 (Hunter) Lecture 9 7 November / 41
11 Third Part: Fill in Missing Pieces 1 Write import statements. 2 Write down new function names and inputs. 3 Fill in return values. 4 Fill in body of functions. import turtle def setup(): newturtle = turtle.turtle() newturtle.penup() return(newturtle) def getinput(): x = int(input( Enter x: )) y = int(input( Enter y: )) return(x,y) def marklocation(t,x,y): t.goto(x,y) t.stamp() def main(): tess = setup() #Returns a purple turtle with pen up. for i in range(5): x,y = getinput() #Asks user for two numbers. CSci marklocation(tess,x,y) 127 (Hunter) #Move Lecture tess 9 to (x,y) and stamp. 7 November / 41
12 Top-Down Design The last example demonstrates top-down design: breaking into subproblems, and implementing each part separately. CSci 127 (Hunter) Lecture 9 7 November / 41
13 Top-Down Design The last example demonstrates top-down design: breaking into subproblems, and implementing each part separately. Break the problem into tasks for a To Do list. CSci 127 (Hunter) Lecture 9 7 November / 41
14 Top-Down Design The last example demonstrates top-down design: breaking into subproblems, and implementing each part separately. Break the problem into tasks for a To Do list. Translate list into function names & inputs/returns. CSci 127 (Hunter) Lecture 9 7 November / 41
15 Top-Down Design The last example demonstrates top-down design: breaking into subproblems, and implementing each part separately. Break the problem into tasks for a To Do list. Translate list into function names & inputs/returns. Implement the functions, one-by-one. CSci 127 (Hunter) Lecture 9 7 November / 41
16 Top-Down Design The last example demonstrates top-down design: breaking into subproblems, and implementing each part separately. Break the problem into tasks for a To Do list. Translate list into function names & inputs/returns. Implement the functions, one-by-one. Excellent approach since you can then test each part separately before adding it to a large program. CSci 127 (Hunter) Lecture 9 7 November / 41
17 Top-Down Design The last example demonstrates top-down design: breaking into subproblems, and implementing each part separately. Break the problem into tasks for a To Do list. Translate list into function names & inputs/returns. Implement the functions, one-by-one. Excellent approach since you can then test each part separately before adding it to a large program. Very common when working with a team: each has their own functions to implement and maintain. CSci 127 (Hunter) Lecture 9 7 November / 41
18 From Last Time: koalas CSci 127 (Hunter) Lecture 9 7 November / 41
19 From Last Time: koalas CSci 127 (Hunter) Lecture 9 7 November / 41
20 From Last Time: koalas CSci 127 (Hunter) Lecture 9 7 November / 41
21 From Last Time: koalas CSci 127 (Hunter) Lecture 9 7 November / 41
22 From Last Time: koalas CSci 127 (Hunter) Lecture 9 7 November / 41
23 From Last Time: koalas Top-down design puzzle: What does koalastomax do? What does each circle represent? Write a high-level design for it. Translate into a main() with function calls. CSci 127 (Hunter) Lecture 9 7 November / 41
24 From Last Time: koalas CSci 127 (Hunter) Lecture 9 7 November / 41
25 From Last Time: koalas CSci 127 (Hunter) Lecture 9 7 November / 41
26 From Last Time: koalas CSci 127 (Hunter) Lecture 9 7 November / 41
27 From Last Time: koalas Top-down design puzzle: What does koalastomax do? What does each circle represent? Write a high-level design for it. Translate into a main() with function calls. CSci 127 (Hunter) Lecture 9 7 November / 41
28 From Last Time: koalas CSci 127 (Hunter) Lecture 9 7 November / 41
29 From Last Time: koalas The main() is written for you. CSci 127 (Hunter) Lecture 9 7 November / 41
30 From Last Time: koalas The main() is written for you. Only fill in two functions: average() and setregion(). CSci 127 (Hunter) Lecture 9 7 November / 41
31 From Last Time: koalas Process: Get template Fill in missing Test locally Submit to from github functions idle3/python3 Gradescope CSci 127 (Hunter) Lecture 9 7 November / 41
32 Today s Topics Recap: Functions & Top Down Design Mapping GIS Data Loops CS Survey CSci 127 (Hunter) Lecture 9 7 November / 41
33 Folium CSci 127 (Hunter) Lecture 9 7 November / 41
34 Folium A module for making HTML maps. CSci 127 (Hunter) Lecture 9 7 November / 41
35 Folium A module for making HTML maps. It s a Python interface to the popular leaflet.js. CSci 127 (Hunter) Lecture 9 7 November / 41
36 Folium A module for making HTML maps. It s a Python interface to the popular leaflet.js. Outputs.html files which you can open in a browser. CSci 127 (Hunter) Lecture 9 7 November / 41
37 Folium A module for making HTML maps. It s a Python interface to the popular leaflet.js. Outputs.html files which you can open in a browser. An extra step: CSci 127 (Hunter) Lecture 9 7 November / 41
38 Folium A module for making HTML maps. It s a Python interface to the popular leaflet.js. Outputs.html files which you can open in a browser. An extra step: Write Run Open.html code. program. in browser. CSci 127 (Hunter) Lecture 9 7 November / 41
39 Demo (Map created by Folium.) CSci 127 (Hunter) Lecture 9 7 November / 41
40 Folium To use: import folium CSci 127 (Hunter) Lecture 9 7 November / 41
41 Folium To use: import folium Create a map: mymap = folium.map() CSci 127 (Hunter) Lecture 9 7 November / 41
42 Folium To use: import folium Create a map: mymap = folium.map() Make markers: newmark = folium.marker([lat,lon],popup=name) CSci 127 (Hunter) Lecture 9 7 November / 41
43 Folium To use: import folium Create a map: mymap = folium.map() Make markers: newmark = folium.marker([lat,lon],popup=name) Add to the map: newmark.add to(mymap) CSci 127 (Hunter) Lecture 9 7 November / 41
44 Folium To use: import folium Create a map: mymap = folium.map() Make markers: newmark = folium.marker([lat,lon],popup=name) Add to the map: newmark.add to(mymap) Many options to customize background map ( tiles ) and markers. CSci 127 (Hunter) Lecture 9 7 November / 41
45 Demo (Python program using Folium.) CSci 127 (Hunter) Lecture 9 7 November / 41
46 In Pairs of Triples Predict which each line of code does: (example from Folium documentation) CSci 127 (Hunter) Lecture 9 7 November / 41
47 Today s Topics Recap: Functions & Top Down Design Mapping GIS Data Loops CS Survey CSci 127 (Hunter) Lecture 9 7 November / 41
48 In Pairs or Triples: Predict what the code will do: CSci 127 (Hunter) Lecture 9 7 November / 41
49 Python Tutor (Demo with pythontutor) CSci 127 (Hunter) Lecture 9 7 November / 41
50 Indefinite Loops CSci 127 (Hunter) Lecture 9 7 November / 41
51 Indefinite Loops CSci 127 (Hunter) Lecture 9 7 November / 41
52 Indefinite Loops Indefinite loops repeat as long as the condition is true. CSci 127 (Hunter) Lecture 9 7 November / 41
53 Indefinite Loops Indefinite loops repeat as long as the condition is true. Could execute the body of the loop zero times, 10 times, infinite number of times. CSci 127 (Hunter) Lecture 9 7 November / 41
54 Indefinite Loops Indefinite loops repeat as long as the condition is true. Could execute the body of the loop zero times, 10 times, infinite number of times. The condition determines how many times. CSci 127 (Hunter) Lecture 9 7 November / 41
55 Indefinite Loops Indefinite loops repeat as long as the condition is true. Could execute the body of the loop zero times, 10 times, infinite number of times. The condition determines how many times. Very useful for checking input, simulations, and games. CSci 127 (Hunter) Lecture 9 7 November / 41
56 Python s random package Python has a built-in package for generating pseudo-random numbers. CSci 127 (Hunter) Lecture 9 7 November / 41
57 Python s random package Python has a built-in package for generating pseudo-random numbers. To use: import random CSci 127 (Hunter) Lecture 9 7 November / 41
58 Python s random package Python has a built-in package for generating pseudo-random numbers. To use: import random Useful command to generate whole numbers: random.randrange(start,stop,step) which gives a number chosen randomly from the specified range. CSci 127 (Hunter) Lecture 9 7 November / 41
59 Python s random package Python has a built-in package for generating pseudo-random numbers. To use: import random Useful command to generate whole numbers: random.randrange(start,stop,step) which gives a number chosen randomly from the specified range. Useful command to generate real numbers: CSci 127 (Hunter) Lecture 9 7 November / 41
60 Python s random package Python has a built-in package for generating pseudo-random numbers. To use: import random Useful command to generate whole numbers: random.randrange(start,stop,step) which gives a number chosen randomly from the specified range. Useful command to generate real numbers: random.random() which gives a number chosen (uniformly) at random from [0.0,1.0). CSci 127 (Hunter) Lecture 9 7 November / 41
61 Python s random package Python has a built-in package for generating pseudo-random numbers. To use: import random Useful command to generate whole numbers: random.randrange(start,stop,step) which gives a number chosen randomly from the specified range. Useful command to generate real numbers: random.random() which gives a number chosen (uniformly) at random from [0.0,1.0). Very useful for simulations, games, and testing. CSci 127 (Hunter) Lecture 9 7 November / 41
62 Trinket (Demo turtle random walk) CSci 127 (Hunter) Lecture 9 7 November / 41
63 Today s Topics Recap: Functions & Top Down Design Mapping GIS Data Loops CS Survey CSci 127 (Hunter) Lecture 9 7 November / 41
64 CS Survey Talk Prof. Susan Epstein (Machine Learning) CSci 127 (Hunter) Lecture 9 7 November / 41
65 Computational system implements decisions and actions on a physical device A computational agent executes a perpetual sense-decide-act loop Do forever Sense the world Select an action Execute that action How to sense the world: infrared sonar radar Kinect microphone camera Given a set of possible actions, an agent decides by selecting one Fall 2018 CSCI 127 1/6 CSci 127 (Hunter) Lecture 9 7 November / 41
66 An AI agent doesn't have to be a robot (embodied in the world) An AI agent doesn't have to be autonomous (make decisions entirely on its own) But it does have to be smart That means it has to make smart decisions Artificial intelligence = simulation of intelligent (human) behavior by a computational agent Do forever Sense the world Select an action Execute that action Nest Controlled by Wi-Fi from a smartphone Reprograms itself based on human behavior Fall 2018 CSCI 127 2/6 2/33 CSci 127 (Hunter) Lecture 9 7 November / 41
67 Tackles hard, interesting problems Does this image show cancer? Should I move this car through the intersection? How do I get to that concert? Builds models of perception, thinking, and action Uses these models to build smarter programs Apollo and ROSie Our autonomous robot navigators Despite uncertainty, noise, and constant changes in the world Learn models of their environment Make smart decisions with those models Fall 2018 CSci 127 (Hunter) Lecture 9 CSCI 127 3/6 7 November / 41
68 We built SemaFORR, a robot controller that makes decisions autonomously First the robots learn to travel by building a model of the world we put them in Then they prove they can find both hard and easy targets there Apollo has already done this on a small part of the 10 th floor here And in simulation ROSie has traveled Through much of Hunter, The Graduate Center, and MOMA Through moving crowds of people Without collision and without coming too close to people And explained her behavior in natural language Fall 2018 CSCI 127 4/6 4 CSci 127 (Hunter) Lecture 9 7 November / 41
69 Find good problems Start simple Run lots of experiments Analyze the results carefully and repeat ithm s s lem lgor so n b a a o e r g p dr r n in Fu n Go o L ea Fall 2018 CSci 127 (Hunter) s CSCI 127 Lecture 9 5/6 5 7 November / 41
70 Fall 2018: SCI 111 Brains, Minds, and Machines = cognitive neuroscience + cognitive psychology + AI Fall 2019: CSCI 350 Artificial Intelligence Fall 2018: CSCI 353 Machine Learning and then there s my lab, where workstations run 24/7, learning to be intelligent agents Susan Epstein, Professor of Computer Science 1090C Hunter North susan.epstein@hunter.cuny.edu Fall 2018 CSCI 127 6/6 6 CSci 127 (Hunter) Lecture 9 7 November / 41
71 Recap On lecture slip, write down a topic you wish we had spent more time (and why). CSci 127 (Hunter) Lecture 9 7 November / 41
72 Recap On lecture slip, write down a topic you wish we had spent more time (and why). Top-down design: breaking into subproblems, and implementing each part separately. CSci 127 (Hunter) Lecture 9 7 November / 41
73 Recap On lecture slip, write down a topic you wish we had spent more time (and why). Top-down design: breaking into subproblems, and implementing each part separately. Excellent approach: can then test each part separately before adding it to a large program. CSci 127 (Hunter) Lecture 9 7 November / 41
74 Recap On lecture slip, write down a topic you wish we had spent more time (and why). Top-down design: breaking into subproblems, and implementing each part separately. Excellent approach: can then test each part separately before adding it to a large program. When possible, design so that your code is flexible to be reused ( code reuse ). CSci 127 (Hunter) Lecture 9 7 November / 41
75 Recap On lecture slip, write down a topic you wish we had spent more time (and why). Top-down design: breaking into subproblems, and implementing each part separately. Excellent approach: can then test each part separately before adding it to a large program. When possible, design so that your code is flexible to be reused ( code reuse ). Introduced a Python library, Folium for creating interactive HTML maps. CSci 127 (Hunter) Lecture 9 7 November / 41
76 Recap On lecture slip, write down a topic you wish we had spent more time (and why). Top-down design: breaking into subproblems, and implementing each part separately. Excellent approach: can then test each part separately before adding it to a large program. When possible, design so that your code is flexible to be reused ( code reuse ). Introduced a Python library, Folium for creating interactive HTML maps. Introduced while loops for repeating commands for an indefinite number of times. CSci 127 (Hunter) Lecture 9 7 November / 41
77 Recap On lecture slip, write down a topic you wish we had spent more time (and why). Top-down design: breaking into subproblems, and implementing each part separately. Excellent approach: can then test each part separately before adding it to a large program. When possible, design so that your code is flexible to be reused ( code reuse ). Introduced a Python library, Folium for creating interactive HTML maps. Introduced while loops for repeating commands for an indefinite number of times. Pass your lecture slips to the aisles for the UTAs to collect. CSci 127 (Hunter) Lecture 9 7 November / 41
78 Practice Quiz & Final Questions Lightning rounds: CSci 127 (Hunter) Lecture 9 7 November / 41
79 Practice Quiz & Final Questions Lightning rounds: write as much you can for 60 seconds; CSci 127 (Hunter) Lecture 9 7 November / 41
80 Practice Quiz & Final Questions Lightning rounds: write as much you can for 60 seconds; followed by answer; and CSci 127 (Hunter) Lecture 9 7 November / 41
81 Practice Quiz & Final Questions Lightning rounds: write as much you can for 60 seconds; followed by answer; and repeat. CSci 127 (Hunter) Lecture 9 7 November / 41
82 Practice Quiz & Final Questions Lightning rounds: write as much you can for 60 seconds; followed by answer; and repeat. Past exams are on the webpage (under Final Exam Information). CSci 127 (Hunter) Lecture 9 7 November / 41
83 Practice Quiz & Final Questions Lightning rounds: write as much you can for 60 seconds; followed by answer; and repeat. Past exams are on the webpage (under Final Exam Information). Theme: Functions & Top-Down Design (Summer 18, #7 & #5). CSci 127 (Hunter) Lecture 9 7 November / 41
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