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 248 • Office hours: In class website (cs.plu.edu/330) • Office Phone: 535-7409 • Email: caora@plu.edu 2 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
About invited speaker Professor from department of Math N. Justice 3
About invited speaker Professor from department of Math Ksenija Simic-Muller 4
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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? 8
What is this course? What is this course about? 9
What is this course? 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? 10
What is this course? Group discussion: • Big changes compared to last year: python, math background, cloud computing. 11
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 = ? 12
What is this course? Machine learning VS traditional programming? 13
Traditional Programming Input two numbers: Data 1 2 Output Machine 1 + 2 = 3 Program
What it cannot solve Rules: Shape? Color? Eye? Mouth? …….
What is this course? Input two numbers: Data 1 2 New Program Machine 2 + 3 = ? 3 output 16
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. 17
Syllabus
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
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) 20
Course website Course Website: • https://www.cs.plu.edu/~caora/cs330/ • https://cs.plu.edu/330 21
Course Goals 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! 22
Learning outcomes 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. 23
Prerequisites 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 24
Course Grade Course participation - 10% • In-class exercises or assignments • Interactions in or out of class • Attendance • Attitude 25
Course Grade Literature Review - 10% • Around one literature review during the semester • Including report and presentation 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. 26
Course Grade Literature Review - 10% • Around one literature review during the semester • Including report and presentation 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. 27
Course Grade Quiz - 20% • Several quizzes during the semester 28
Course Grade Labs - 25% • Around 4 labs during the semester 29
Course Grade Final exam - 15% • One final written exam 30
Course Grade 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 31
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
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.
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
Introduction 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 35
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. Machine learning in now days 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.
Introduction 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. 37
Introduction 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. 38
Introduction 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. 39
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