Intro to Machine Learning

1. Intro to Machine Learning • Mark Stamp Introduction to Machine Learning

2. What is Machine Learning? Introduction to Machine Learning • Definition of machine learning (ML)? • Our working definition is… • Statistical discrimination, wherethe “machine” does the hard work (“learns”) • So, we humans don’t have to think too much • Often associated with AI • But actually much more widely applicable • Usually, based on a binary classifier • Often said to generate “data driven” models

3. What Can MLDo for Me? Introduction to Machine Learning • Machine learning is very powerful • Practical and useful • Successfully applied to problems in… • Speech recognition and NLP • bioinformatics • stock market analysis • AI (robotics, computer vision, etc.) • More and more and more uses all the time

4. Black Box Approach Introduction to Machine Learning • Machine learning (ML) algorithm often treated as a black box • This is one of ML’s main selling points! • ML black box often works well • You can get good results even if you know nothing about underlying algorithms • But, this can be limiting • Especially innew and novel applications

5. Analogy to a Doctor Introduction to Machine Learning NP is a nurse with advanced training Physician has much more education Both diagnose, treat, and manage patients’ problems Studies show that NPs can do about 80% to 90% of what physicians do But for the most challenging 10% to 20% of cases, a physician is required

6. Interesting, But What Does This Have to Do with ML? Introduction to Machine Learning • ML version of NP would have knowledge beyond black box, but not too much • ML version of physician would really understand how/why things work • Goal is for you to become ML physician • For doctors, most challenging 10% to 20% of cases are most interesting… • …and the most lucrative!

7. Auto Mechanic Analogy Introduction to Machine Learning • The majority of diagnosis work done by auto mechanics is routine • Often easy to see what the problem is (not necessarilyeasy to fix the problem) • But, there are some difficult cases • Where no “cookbook” diagnosis will work • Skill needed to analyze such problems • Requires deep understanding of inner workings of engine and related systems

8. ML from 10,000 Feet Introduction to Machine Learning • Usually focus on binary classification • First, we train a model on set of samplesof, say, “type A” • Then given sample of unknown type • Score the sample against the model • If it scores high, classify it as “type A” • Otherwise, classify it as “not type A” • Key ideasare training and scoring

9. Topics Covered in Detail Introduction to Machine Learning • Hidden Markov Model (HMM) • Profile Hidden Markov Model (PHMM) • Principal Component Analysis (PCA) • And Singular Value Decomposition (SVD) • Support Vector Machine (SVM) • Clustering (emphasis on K-means and EM) • Neural networks (ANN) • Backpropagation and a lot more • Data analysis

10. Many Mini-Topics Introduction to Machine Learning k-nearest neighbor (k-NN) Boosting (AdaBoost) Random Forests (RF) Linear Discriminant Analysis (LDA) Vector quantization (VQ) Naïve Bayes and regression analysis Conditional Random Fields (CRF)

11. HMM Introduction to Machine Learning • We cover HMMs in greatest detail • More detail than other ML techniques • You must implement HMM from scratch • You must understand it well • Compare/contrast everythingto HMM • HMMs useful in many applications • And models themselves tell us something • Not always true of other algorithms

12. High Level View of HMM Introduction to Machine Learning Markov process, where states “hidden” Observations related to hidden states

13. HMM as Hill Climb • In contrast to heuristic search, such as genetic algorithm or simulated annealing • Advantage(s) of hill climb algorithm? • Disadvantages/limitations of hill climb? • Can we overcome disadvantages? Introduction to Machine Learning • Hill climb on parameter space • What is a hill climb? • Only go “up”, never “down”

14. PHMM Introduction to Machine Learning • Like HMM with positional information • Conceptually appealing • Details tend to be very problem specific • Widely used in bioinformatics • And other applications where position within sequence is critical information • Has been used successfully in security research (IDS, malware detection)

15. High Level View of PHMM Introduction to Machine Learning • Like defining a (particularly simple) HMM at each position in sequence • Easier to understand once we study HMM

16. PCA Introduction to Machine Learning • PCA serves to reduce dimensionality • Training is complex (lots of math) • But scoring is fast and efficient • So, when the dust settles, PCA is actually easy to apply and very efficient • Singular Value Decomposition (SVD) (almost) synonymous with PCA • SVD is one way to train a model in PCA

17. High Level View of PCA Introduction to Machine Learning • Finddirections with highest variance • Reveals useful info… • …dimensionality can be reduced • Why high variance? • Need lots oflinear algebra here…

18. SVM Introduction to Machine Learning • SVM has nice geometric interpretation • We can draw somepretty pictures! • In SVM, we increase the dimension • May be counterintuitive(compare to PCA) • SVM often used similar to other ML • But also ideal as a “meta-score” to combine multiple other scores • Great combination of theory, practice

19. High Level View of SVM Introduction to Machine Learning • Labeled training data • Separate the sets… • …and maximize margin • Easy to picture • We use lots ofcalculus to make sense of SVM • Most challenging derivationwe consider?

20. Clustering Introduction to Machine Learning • Usually, used for “data exploration” • I.e.,cluster hoping to discern structure from data that we know little about • Observed structure may or may not be meaningful (can cluster anything) • We consider 2 clustering techniques • K-means • EM (expectation maximization)

21. Clustering Example Introduction to Machine Learning • Unsupervised • Data exploration • K-means is easy and intuitive • EM more challenging • Some statistics… • Multivariate Gaussian distributions!

22. Neural Networks Introduction to Machine Learning • Artificial neural network (ANN) • A “mini topic” in the book • Now a major topic of the course • Focus is on backpropagation • Technique used to train neural networks • Essentially, a big calculus problem • And cover some of “alphabet soup” • RNN, CNN, LSTM, GAN, etc.

23. Mini Topics++ Introduction to Machine Learning • Boosting • Make arbitrarily strong classifier from many (weak) classifiers • Focus on AdaBoost (Adaptive Boosting) • Linear Discriminant Analysis (LDA) • Discuss this in some detail • Interesting connections to PCA and SVM

24. Mini Topics Introduction to Machine Learning • Random forest (RF) • Very popular and useful • Based on decision trees • We only cover the basics • K-nearest neighbor (k-NN) • Simplest “machine learning” imaginable • Vector quantization (VQ) • A generalization of K-means clustering

25. Mini Topics-- Introduction to Machine Learning • Conditional Random Fields (CRF) • Generalization of HMM • Interesting, but not so practical • Probably won’t have time for this • Naïve Bayes and regression analysis • And related statistical techniques • Interesting and practical • But, probably not enough time

26. The Dreaded Math… Introduction to Machine Learning • Striveto keep math to a minimum • Course is (mostly) self-contained wrt math • First semester calculus is assumed • To understand ML, cannot avoid math... • HMM/PHMM  discrete probability • PCA fancy linear algebra (eigenvectors) • SVM  calculus (Lagrange multipliers) • Clustering  statistics/probability • Backpropagation calculus (computational)

27. Data Analysis Introduction to Machine Learning • Critical to analyze data carefully • Especially true in research mode, as we must compare to previous work • Often, a major weakness in research! • We’ll discuss… • Experimental design, cross validation, accuracy, ROC curves, PR curves, imbalance problem, and so on

28. Applications Introduction to Machine Learning • Applications mostly from security • Malware detection or analysis  HMM, PHMM, PCA, SVM, and clustering • Masquerade detection  PHMM • Image spam  PCA and SVM • Classic cryptanalysis  HMM • Facial recognition  PCA • Text analysis  HMM • Old Faithful geyser  clustering

29. 3 Stages of ML Enlightenment Introduction to Machine Learning • First stage elementary-school level • “Big picture” from 10k feet • See the descriptions in this intro • Second stage  drill down on big picture • More detailed and nuanced than first stage • Understand the pictures used in stage 1 • Third stage  real understanding • Learn derivations and (mostly) understand it

30. ML Enlightenment Introduction to Machine Learning • In this class, we aim for highest stage of ML enlightenment • But to pass the class, at a minimum, must have stage 2++ knowledge… • …and ability to effectively use ML … • … andunderstand strengths/weaknesses • Key to success • Work hard on homework and project!

31. Bottom Line Introduction to Machine Learning • We cover selected machine learning techniques in considerable detail • We discuss many applications, mostlyrelated to information security • Goal is for students to gain a deep understanding of the techniques • And be able to apply ML… • …especially in new and novel situations

32. How to Succeed in ML Class Introduction to Machine Learning • Ask questions • Good questions are good for everybody • Treat the math asyour friend • Math is needed to make sense of ML • Do not fear hard work! • Machine learning is not a spectator sport • Learning is a 3 step process • Read book, attend lecture, do homework