CSCI 4520: Introduction to Machine Learning Spring 2020 Mehdi Allahyari Georgia Southern University (some slides from Pedro Domingos, University of Washington) 1
Mehdi Allahyari n Call me Dr. Yari n PhD from UGA n Machine Learning, Semantic Web, Topic Modeling, Knowledge Graph 2
Course Information CSCI 4520 - Introduction to Machine Learning Spring 2020 – CRN: 17186 Instructor: Dr. Mehdi Allahyari < mallahyari@georgiasouthern.edu > O ffi ce: CEIT 2321 Course Webpage: https://sci2lab.github.io/mehdi/teaching/cs4520 Lecture Times Tuesday Thursday 12:30 PM – 01:45 PM 12:30 PM – 01:45 PM CRN 17186 CEIT 2207 CEIT 2207 O ffi ce Hours Monday Wednesday 11:00 AM – 12:00 PM 11:00 AM – 12:00 PM Tuesday Thursday or by appointment 11:00 AM – 12:00 PM 11:00 AM – 12:00 PM CEIT 2321 CEIT 2321 3
Text Books ( Highly Recommended) 4
Other Great Text Books 5
Resources n Course page: https://sci2lab.github.io/mehdi/teaching/cs4520/ n is where you get course content (syllabus, lectures, etc.) n Folio: n Folio is the website where you will submit all assignments (if online), see your grades 6
Course Description n This introductory course is designed: n to give upper level undergraduate a broad overview of many concepts and algorithms in ML. n covers the theory and practical algorithms for machine learning from a variety of perspectives. n Topics: n Statistical and probabilistic methods, generative and discriminative models, linear and logistic regression, decision tree learning, unsupervised learning and clustering and dimensionality reduction. n In addition, the course covers fundamental concepts such as training, validation, overfitting, and error rates 7
Prerequisites n Basics knowledge of probability, linear algebra and calculus. n For example, standard probability distributions and also how to calculate derivatives. n Significance programming experience n Python is the language we’ll use 8
Grading n Attendance and participation: 5% n Homeworks: 20% n Four homeworks: submitted hardcopy and on Folio n Some exercises, significant programming n Midterm 03/05/2020 20% n Final exam 05/05/2020 12:30 – 2:30 PM. 25% n Final project: 30% n Teams of 2 n Define a task, create/acquire data for the task, train ML algorithm(s), evaluate & report n Check the course website for details A ≥ 85 85 > B ≥ 80 80 > C ≥ 70 70 > D ≥ 65 F < 65 9
Homeworks Deadlines n Assignments are usually due 10-14 days (see course schedule for deadlines) n Late submissions for labs/projects are penalized n 0-24 hours late -10 points n 24-48 hours late -20 points n >48 hours late 0 n Forgetting to submit is not a valid excuse n Double check your submission n Save your confirmation email 10
Collaboration Policy & Academic Honesty n Homeworks to be done individually n Your own answer and code n can collaborate in figuring out answers and helping each other solve the problems at high level n No sharing of pseudocode or code or simulation results n Academic Honesty: n abide by the University’s academic honesty policy and the Student Honor Code n Lack of knowledge of the academic honesty policy is not a reasonable explanation for a violation 11
Syllabus Questions? n Familiarize yourself with the syllabus n This PowerPoint highlights key information from the syllabus but does not cover every subject in detail n You are responsible for the entirety of the syllabus n Your HW is to go home & read the syllabus thoroughly 12
A Few Quotes n “A breakthrough in machine learning would be worth ten Microsofts” (Bill Gates, Founder, Microsoft) n “Machine learning is the next Internet” (Tony Tether, Director, DARPA) n Machine learning is the hot new thing” (John Hennessy, President, Stanford) n “Machine learning is Google’s top priority” (Eric Schmidt, Chairman, Alphabet) n “Machine learning is Microsoft Research’s largest investment area” (Peter Lee, Head, Microsoft Research) n “‘Data scientist’ is the hottest job title in Silicon Valley” (Tim O’Reilly, Founder, O’Reilly Media) 13
What is Machine Learning? n How can we solve a specific problem? n As computer scientists we write a program that encodes a set of rules that are useful to solve the problem n In many cases is very difficult to specify those rules, e.g., given a picture determine whether there is a cat in the image 14
What is Machine Learning? n Learning systems are not directly programmed to solve a problem, instead develop own program based on: n Examples of how they should behave n From trial-and-error experience trying to solve the problem n Different than standard CS n Want to implement unknown function, only have access e.g., to sample input-output pairs (training examples) n Learning simply means incorporating information from the training examples into the system 15
What is Machine Learning? n “The study of computer programs thatimprove automatically with experience” n T. Mitchell Machine Learning n Getting computers to program themselves n Writing software is the bottleneck, Let the data do the work instead! 16
Traditional Programming Input Output Computer Program Machine Learning Input Program Computer Output 17
Magic? No, more likegardening } Seeds = Algorithms } Nutrients = Data } Gardener = Y ou } Plants = Programs 18
Sample Applications n Web search n Computational biology n Finance n E-commerce n Space exploration n Robotics n Information extraction n Social networks n Debugging n [Your favorite area] 19
Tasks that requires machine learning: What makes a 2? 20
Why use machine learning? n It is very hard to write programs that solve problems like recognizing a handwritten digit n What distinguishes a 2 from a 7? n How does our brain do it? n Instead of writing a program by hand, we collect examples that specify the correct output for a given input n A machine learning algorithm then takes these examples and produces a program that does the job n The program produced by the learning algorithm may look very different from a typical hand-written program. It may contain millions of numbers. n If we do it right, the program works for new cases as well as the ones we trained it on. 21
Relationship of Machine Learning to… n Statistics n Analytics / Data Science n Data Mining n Artificial Intelligence 22
ML in Practice n Understanding domain, prior knowledge, and goals n Data integration, selection, cleaning n pre-processing, etc. n Learning models n Interpreting results n Consolidating and deploying discovered knowledge n Loop 23
ML in Nutshell n Hundreds of machine learning algorithms n Tens new every year n Every machine learning algorithm has three components: n Representation n Evaluation n Optimization 24
Representation n How do we represent the function from input to output? n Decision trees n Sets of rules / Logic programs n Graphical models (Bayes/Markov nets) n Neural networks n Support vector machines n Model ensembles n Etc. 25
Evaluation n Given some data, how can we tell if a function is “good”? n Accuracy n Precision and recall n Squared error n Likelihood n Posterior probability n Cost / Utility n Margin n Entropy n K-L divergence n Etc. 26
Optimization n Given some data, how do we find the “best” function? n Combinatorial optimization n E.g.: Greedy search n Convex optimization n E.g.: Gradient descent n Constrained optimization n E.g.: Linear programming 27
Learning algorithms are useful in many tasks 1. Classification: Determine which discrete category the example is 28
Examples of Classification What digit is this? 29
Examples of Classification Is this a dog? 30
Examples of Classification What about this one? 31
Examples of Classification Am I going to pass the exam? 32
Examples of Classification Do I have diabetes? 33
Learning algorithms are useful in many tasks 1. Classification: Determine which discrete category the example is 2. Recognizing patterns: Speech Recognition, facial identity, etc 34
Examples of Recognizing patterns Figure: Siri: https://www.youtube.com/watch?v=8ciagGASro0 35
Examples of Recognizing patterns Figure: Photomath: https://photomath.net/ 36
Learning algorithms are useful in many tasks 1. Classification: Determine which discrete category the example is 2. Recognizing patterns: Speech Recognition, facial identity, etc Recommender Systems: Noisy data, commercial 3. pay-off (e.g., Amazon, Netflix). 37
Examples of Recommendation systems 38
Examples of Recommendation systems 39
Examples of Information Retrieval 40
Examples of Information Retrieval 41
Examples of Information Retrieval 42
Learning algorithms are useful in many tasks 1. Classification: Determine which discrete category the example is 2. Recognizing patterns: Speech Recognition, facial identity, etc Recommender Systems: Noisy data, commercial 3. pay-off (e.g., Amazon, Netflix). 4. Information retrieval: Find documents or images with similar content 5. Computer vision: detection, segmentation, depth estimation, optical flow, etc 43
Examples of Computer Vision https://blog.insightdatascience.com/generating-custom-photo-realistic-faces-using-ai-d170b1b59255 44
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