CS 330 - Artificial Intelligence - Introduction Instructor: Renzhi - - PowerPoint PPT Presentation

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CS 330 - Artificial Intelligence - Introduction Instructor: Renzhi - - PowerPoint PPT Presentation

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1 CS 330 - Artificial Intelligence - Introduction Instructor: Renzhi Cao Computer Science Department Pacific Lutheran University Fall 2019 About me Renzhi Cao Data Science Machine learning Bioinformatics Office: MCLT

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CS 330 - Artificial Intelligence

  • Introduction

Instructor: Renzhi Cao Computer Science Department Pacific Lutheran University Fall 2019 1

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  • Office: MCLT 248
  • Office hours: In class website (cs.plu.edu/330)
  • Office Phone: 535-7409
  • Email: caora@plu.edu

About me

Renzhi Cao

  • Data Science
  • Machine learning
  • Bioinformatics

Pictures from: https://www.google.com/search?q=cow&biw=1920&bih=911&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiOt5zlierOAhUE02MKHVbwDY8Q_AUIBigB#imgrc=0dSVh7Vlup1KqM %3A

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About invited speaker

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Professor from department of Math

  • N. Justice
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About invited speaker

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Professor from department of Math Ksenija Simic-Muller

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About you

  • Names
  • Where are you from?
  • Your major?
  • Hobbies? Movie? Song? Sports? Book? TV show?

What you did over your break? …

  • Special skills?

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What is this course?

What is this course about?

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What is this course?

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Group discussion:

  • Think an example of AI applications that you could

thought of, and share with your neighbors.

  • Think one scenario that AI may not work or you don’t

want it work? Share with your neighbors.

  • Your new understanding of AI?
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What is this course?

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Group discussion:

  • Big changes compared to last year: python, math

background, cloud computing.

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What is this course?

How do you solve a problem as a computer programmer? For example, I have seen the following: 1 + 2 = 3 2 + 3 = 5 5 + 4 = 9 …… Q: 4 + 5 = ? 5 + 6 = ?

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What is this course?

Machine learning VS traditional programming?

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Traditional Programming

Data Program Machine Output

1 + 2 = 3

Input two numbers: 1 2

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What it cannot solve

Rules: Shape? Color? Eye? Mouth? …….

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What is this course?

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Data

  • utput

Machine New Program 3

Input two numbers: 1 2

2 + 3 = ?

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What is this course?

In this course, we are going to learn several machine learning techniques in AI, and use it to solve problems in different fields.

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Syllabus

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Attendance

Attendance

  • Expected to attend every class
  • YOU are responsible for missed materials

Classroom Conduct

  • Come to class on time
  • Turn off electronic devices
  • Refrain from private conversations (voice or electronic)
  • Refrain from activities unrelated to current tasks in class
  • Treat others with respect and dignity
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Text book and meeting times

Books:

  • Machine Learning. By Tom M. Mitchell, PUBLISHED BY MIT PRESS, 1997.
  • Introduction to machine learning. BY ALPAYDIN, ETHEM, PUBLISHED BY MIT

PRESS, 2009

  • Deep Learning. BY GOODFELLOW, IAN, PUBLISHED BY MIT PRESS, 2016

Time:

  • Tuesday, Thursday 15:40-17:25, MCLT #203 (Dr. Cao)
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Course website

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Course Website:

  • https://www.cs.plu.edu/~caora/cs330/
  • https://cs.plu.edu/330
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Course Goals

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Course goals:

  • Understanding machine learning concept
  • Developing problem solving skills
  • Applying machine learning techniques to solve problems in different fields
  • Having fun on machine learning techniques and developing skills that will

allow you to learn on your own!

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Learning outcomes

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Learning outcomes:

  • An ability to apply mathematics and the scientific method to solving computing

problems.

  • An ability to critically analyze a problem and to design, implement, and evaluate

a computing solution that meets requirements.

  • An ability to work effectively in small groups on medium scale computing

projects.

  • An ability to use oral and written communication effectively.
  • A recognition of the need to engage in life long learning.
  • An ability to understand the social and ethical implications of working as a

professional in the field of computer science.

  • An ability to use current tools and methodologies in computing practice.
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Prerequisites

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The official prerequisite for this course is Data Structure in CS 270. Students in Data Science minor could take DS 233 as prerequisite. Some programming experience is preferred, and math background is plus:

  • Linear algebra: vector/matrix manipulations, properties
  • Calculus: partial derivatives
  • Probability: common distributions; Bayes Rule
  • Statistics: mean/median/mode; maximum likelihood
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Course Grade

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Course participation - 10%

  • In-class exercises or assignments
  • Interactions in or out of class
  • Attendance
  • Attitude
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Course Grade

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Literatures will be provided by Professor Cao, but you can also find your own literature as long as you send it to Professor Cao before presentation for

  • approval. It is group work, and each group needs to select a topic from a list.

Each group should email me if you decide to present a literature and topic as soon as possible. Ideally, each literature and topic should be presented by one group, and 10% deduction may be applied to other groups to present the same topic and literature. The reports of literature review would be summary of literatures on the selected topic as a report, and slides of group presentation.

Literature Review - 10%

  • Around one literature review during the semester
  • Including report and presentation
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Course Grade

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The literature review will be around 25 mins (20 mins presentation and 5 mins Q&A) for each group, and will be evaluated by the following factor:

  • Clearly present the background and its significance or motivation? (3 points)
  • Slides is clear and easy to follow. (3 points)
  • Present smoothly. You may need to practice ahead. (3 points)
  • The time. Not too early or too late. (3 points)
  • The literature itself. Some are easy to present, some are difficult. (3 points)
  • Extra points is available if you are actively interacting with other group’s

presentation.

Literature Review - 10%

  • Around one literature review during the semester
  • Including report and presentation
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Course Grade

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Quiz - 20%

  • Several quizzes during the semester
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Course Grade

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Labs - 25%

  • Around 4 labs during the semester
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Course Grade

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Final exam - 15%

  • One final written exam
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Course Grade

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Course Project - 20%

  • Mid-term proposal presentation (5%)
  • Final project result presentation (5%)
  • Reports and codes (10%)
  • Extra points based on novelty and performance of your

methods

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Overall Score Grade 100% -- 90% A / A- 90% -- 80% B+ / B / B- 80% -- 70% C+ / C / C- 70% -- 60% D+ / D / D- 60% -- 0% E

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Policies of collaborations and assignments

Not allowed for assignments or in-class exercises. Allowed for project or literature review, but contributions of each person need to be included in report. Cite references and acknowledge others work. If students begin working on a project as groups and cannot complete it together, at least one student must contact the instructor to request a partnership dissolution. Assignments or in-class exercises must be submitted before the due date. A late penalty of 10% per day will be assessed after due date, except that you have a strong reason - an emergency, illness, or absence due to a university sanctioned activity such as a sporting event or music performance.

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Important time

  • Last day to add a class without a fee: Sep. 13th
  • Last day to drop a class without a fee: Sep. 20th
  • Last day to withdraw: Nov. 29th
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Introduction

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When machine learning starts?

Inventors have long dreamed of creating machines that can learn. Desires date back to at least the time of ancient Greece. Inventors Pygmalion and statue he carved - Galatea Talos - a giant automaton made of bronze to protect Europa in Crete from pirates and invaders, by inventor Daedalus Inventor Hephaestus and the first human woman created by him - Pandora

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Introduction

When machine learning starts?

People wonder whether such machines may become intelligent. Today, Artificial intelligence (AI) is a thriving field with many practical applications and active research topics.

Machine learning in early days

Used to solve problems that are intellectually difficult for human beings but relatively straightforward for computers, based on a list of formal and mathematical rules. The true challenge for machine learning is to solve tasks that are easy for people but difficult for machine to do. Recognizing spoken words, faces in images.

Machine learning in now days

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Introduction

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Why machine learning?

  • My experience at Silicon Valley. (13 w)
  • It’s everywhere. You may not want to know how to make a

car, but it’s always good if you know something about it.

  • A dream that one day the machine is as intelligent as

human.

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Introduction

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Why machine learning?

More importantly, the traditional program can not solve some problems, such as recognizing a three-dimensional object from a novel view in new lighting conditions in a cluttered scene.

  • What to write?
  • Even you know what to

write, it’s too complicate.

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Introduction

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Why machine learning?

What rule to decide your credit card transaction is fraudulent?

  • There may be simple rules, but we need to

combine a lot of weak rules

  • Rules will be changed, so your method needs to

be updated all the time.

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Introduction

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Machine learning approach

Instead of writing program with all different rules, we collect examples that specify the correct output for a given input.

  • The machine created program may look very different from typical
  • program. It may contains millions of numbers
  • If we do it right, the machine created program works well for new

cases, and also the ones we trained on it

  • If the data changes, the program can also be changed by training on

new data

A machine learning algorithm takes the examples and produces program that does the job Massive amounts of computation are now cheaper than paying someone to write-specific program.

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Spam Filtering Web Search Postal Mail Routing Fraud Detection

Movie Recommendations

Vehicle Driver Assistance Web Advertisements Social Networks Speech Recognition

Machine Learning in Our Daily Lives

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Introduction

Google search demo

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Introduction

Google translation demo

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Introduction

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What is this course?

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Introduction

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Introduction

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Self-driving car

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What is this course?

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Introduction

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Amazon recommendation

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Art

Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." arXiv preprint arXiv:1603.08155 (2016).

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Art

Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." arXiv preprint arXiv:1603.08155 (2016).

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Art

Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." arXiv preprint arXiv:1603.08155 (2016).

The Muse, Pablo Picasso, 1935

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Computational biology

>T0759 HR9083A, Human, 109 residues MGHHHHHHSHMVVIHPDPGRELSPEEAHRAGLIDWNMFVKLRSQECDWEEISVKGPNGES SVIHDRKSGKKFSIEEALQSGRLTPAHYDRYVNKDMSIQELAVLVSGQK

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More interesting AI

  • http://simon.cs.plu.edu/MLFun//

index.php

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Introduction

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Machine VS Human

  • IBM deep blue chess-playing system

defeated world champion Garry Kasparov in 1997.

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March 2016

AlphaGo 4 – Lee Sedol 1

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https://www.britgo.org/intro/intro2.html

The rules

  • Starts with an empty board.
  • Players take turns to place one stone on vacant point
  • Capture your opponent’s stones by completely surrounding them
  • Goal: Use your stones to form territories by surrounding vacant

areas of the board

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Baidu’s AI boss, Andrew Ng, pictured left with the host of ‘Super Brain’ and Baidu’s robot, Xiaodu

  • Jan. 2017
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https://www.theguardian.com/technology/2017/jan/30/libratus-poker-artificial-intelligence-professional-human-players-competition

  • Jan. 2017
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SWARM AI CORRECTLY PREDICTED THE OUTCOME OF SUPER BOWL LI, RIGHT DOWN TO THE FINAL SCORE

http://www.digitaltrends.com/cool-tech/swarm-artificial-intelligence-super-bowl-patriots/

The New England Patriots’ win over the Atlanta Falcons was nothing short of

  • amazing. The Pats rallied back from a 25-point deficit to tie the game in the final

minutes of regulation and secured the win with a decisive touchdown drive in

  • vertime.

Swarm AI (Combines swarming algorithms with human input) accurately predicted the outcome of the game, right down to the 34-28 win by the Patriots. February 6, 2017

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Machine learning VS AI

  • Interesting talk with students
  • Artificial Intelligence is the broader concept of machines being

able to carry out tasks in a way that we would consider “smart”.

  • Machine Learning is a current application of AI based around the idea

that we should really just be able to give machines access to data and let them learn for themselves.

http://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#2d597284687c

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AI Machine learning Representation learning Deep learning Example: Knowledge bases Example: Logistic regression Example: Shallow autoencoders Example: MLPs

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The MNIST Dataset

Goodfellow, 2016

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Introduction

Historical Trends: Growing Datasets

1900 1950 1985 2000 2015 100 101 102 103 104 105 106 107 108 109 Dataset size (number examples) Iris MNIST Public SVHN ImageNet CIFAR-10 ImageNet10k ILSVRC 2014 Sports-1M Rotated T vs. C T vs. G vs. F Criminals Canadian Hansard WMT

Figure 1.8

Goodfellow, 2016

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Biological neural network size from Wikipedia (2015).

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Introduction

Number of Neurons

Figure 1.11

Goodfellow, 2016

1950 1985 2000 2015 2056 10−2 10−1 100 101 102 103 104 105 106 107 108 109 1010 1011 Number of neurons (logarithmic scale) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Sponge Roundworm Leech Ant Bee Frog Octopus Human

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Introduction

Solving Object Recognition

Figure 1.12

Goodfellow, 2016

2010 2011 2012 2013 2014 2015 0.00 0.05 0.10 0.15 0.20 0.25 0.30 ILSVRC classification error rate

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Useful resources

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There will be several useful resources:

  • Sakai for announcement and assignments
  • Course website : https://cs.plu.edu/330
  • GitLab: full featured GitHub style system, but it is self-hosted on PLU servers

https://gitlab.cs.plu.edu

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Useful resources

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Resources

Before you leave today…


  • apply for a curly account

https://www.cs.plu.edu/hub/accounts/requests/new Finish survey about your background (available on course website). Register and change your password on course website.


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Resources

Create an Account… – Open the Firefox browser – Go to https://www.cs.plu.edu/hub/ – Click on Request link – Review PLU Policies – Click on I agree link

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Resources

Account for the first few days (active through Sep. 21, 2019):

user name: firstday password: fall2019

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Topics (Theme of AI in Business)

  • AI in Accounting
  • AI in Finance
  • AI in Marketing
  • AI in HR
  • AI in Cyber security like Cryptocurrency
  • AI in Healthcare (Bioinformatics)
  • AI in supply-chain and logistics (Amazon)
  • AI in Autonomous car
  • Other AI applications in Business
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Tasks in summary

  • 2. Think about topics that you are interested and form group,

Proposing interesting machine learning topics.

  • 1. Explore gitlab / github
  • 3. Read materials in course website
  • 4. Labs, and your computer availability (Python programming)
  • 5. Request account before first day account expired
  • 6. Finish survey, check out class website and Sakai regularly:

https://www.cs.plu.edu/~caora/cs330/

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Questions

Discussion about topics