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Week 1 - An Introduction to Machine Learning & Soft Computing -Yosi Kristian- Soft Computing STTS – Yosi Kristian 2 Definition • Soft Computing is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally hard tasks, for which there is no known algorithm that can compute an exact solution. • Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. Soft Computing STTS – Yosi Kristian 3 Still the Definition.. • In effect, the role model for soft computing is the human mind. • The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. Soft Computing STTS – Yosi Kristian 4 Components of soft computing • • • • Neural networks (NN) Support Vector Machines (SVM) Fuzzy logics (FL) Evolutionary computation (EC), including: What ??? o Evolutionary algorithms o Are we going to learn them all Meta heuristic and Swarm Intelligence • Ant colony optimization in this subject? • Genetic algorithms • Differential evolution • • • • • • • Bees algorithms Bat algorithm Cuckoo search Harmony search Firefly algorithm Artificial immune systems Particle swarm optimization Soft Computing STTS – Yosi Kristian 5 Soft Computing in AI • Soft computing may be viewed as a foundation component for the emerging field of conceptual intelligence. o o o o Machine Learning Fuzzy Systems Evolutionary Computation Probabilistic Reasoning • Soft Computing is the CORE component of many Machine Learning System Soft Computing STTS – Yosi Kristian 6 Machine Learning • Arthur Samuel (1959). Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed. • Tom Mitchell (1998) Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. Soft Computing STTS – Yosi Kristian 7 Machine learning usage • Usage of Machine Learning is to develop applications that can’t be programed by hand. • E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing (NLP), Computer Vision etc. • Or a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and nonspam folders. Soft Computing STTS – Yosi Kristian 8 Machine Learning Categorized By Data and Learning Process Soft Computing STTS – Yosi Kristian 9 Soft Computing In Machine Learning • Soft Computing is the soul of many machine learning system. • Classification and Clustering is a very common soft computing problems. Soft Computing STTS – Yosi Kristian 10 Intro to Supervised Learning Soft Computing STTS – Yosi Kristian 11 Example Housing price prediction. 400 300 Price ($) 200 in 1000’s 100 0 0 500 1000 1500 2000 2500 Size in feet2 Supervised Learning “right answers” given Soft Computing STTS – Yosi Kristian Regression: Predict continuous valued output (price) 12 Example Breast cancer (malignant, benign) Classification Discrete valued output (0 or 1) 1(Y) Malignant? 0(N) Tumor Size Tumor Size Soft Computing STTS – Yosi Kristian 13 Another Example - Clump Thickness - Uniformity of Cell Size - Uniformity of Cell Shape … Age Tumor Size Soft Computing STTS – Yosi Kristian 14 Exercise • You’re running a company, and you want to develop learning algorithms to address each of two problems. • Problem 1: You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months. • Problem 2: You’d like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised. • Should you treat these as classification or as regression problems? Soft Computing STTS – Yosi Kristian 15 Answer • Treat both as classification problems. • Treat problem 1 as a classification problem, problem 2 as a regression problem. • Treat problem 1 as a regression problem, problem 2 as a classification problem. • Treat both as regression problems. Soft Computing STTS – Yosi Kristian 16 Classification Example • Another Example is for image Classification / Categorization Training Training Images Image Features Training Labels Classifier Training Trained Classifier Soft Computing STTS – Yosi Kristian 17 Cont… Testing Image Features Trained Classifier Test Image Prediction Outdoor Soft Computing STTS – Yosi Kristian 18 Learning a classifier • Given some set of features with corresponding labels, learn a function to predict the labels from the features • Training labels dictate that two examples are the same or different, in some sense • Features and distance measures define similarity • Classifiers try to learn weights or parameters for features and distance measures so that feature similarity predicts label similarity Soft Computing STTS – Yosi Kristian 19 Intro to Unsupervised Learning Soft Computing STTS – Yosi Kristian 20 Supervised Learning x2 x1 Soft Computing STTS – Yosi Kristian 21 Unsupervised Learning x2 x1 Soft Computing STTS – Yosi Kristian 22 Clustering Example Soft Computing STTS – Yosi Kristian 23 Contd… Soft Computing STTS – Yosi Kristian 24 Exercise Of the following examples, which would you address using an unsupervised learning algorithm? (Check all that apply.) o Given email labeled as spam/not spam, learn a spam filter. o Given a set of news articles found on the web, group them into set of articles about the same story. o Given a database of customer data, automatically discover market segments and group customers into different market segments. o Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not. Soft Computing STTS – Yosi Kristian 25 Warming Up…. • Do 10 x Push Ups. Soft Computing STTS – Yosi Kristian 26 Linear Regression with one variable Housing Prices (Portland, OR) 500 400 300 Price 200 (in 1000s 100 of dollars) 0 0 500 1000 1500 2000 2500 3000 Size (feet2) Supervised Learning Regression Problem Given the “right answer” for each example in the data. Predict real-valued output Soft Computing STTS – Yosi Kristian 27 Linear Regression with one variable Training set of housing prices (Portland, OR) Notation: m = Number of training examples n = Number of feature x’s = “input” variable / features y’s = “output” variable / “target” variable Soft Computing STTS – Yosi Kristian 28 The Concept How do we represent h ? Training Set Learning Algorithm Size of house h Estimate d price Linear regression with one variable. Univariate linear regression. Soft Computing STTS – Yosi Kristian 29 Cost Function Training Set Hypothesis: ‘s: Parameters How to choose Soft Computing STTS – Yosi Kristian ‘s ? 30 Contd.. 3 3 3 2 2 2 1 1 1 0 0 0 0 1 2 3 Soft Computing STTS – Yosi Kristian 0 1 2 3 0 1 2 3 31 Cost Function.. y x Idea: Choose so that is close to for our training examples Soft Computing STTS – Yosi Kristian 32 Simplification: For the sake of understanding Hypothesis: Simplified Parameters: Cost Function: Goal: Soft Computing STTS – Yosi Kristian 33 Trial 1 (for fixed , this is a function of x) (function of the parameter 3 3 2 2 1 1 0 0 -0.5 0 y 0 1 x 2 Soft Computing STTS – Yosi Kristian 3 0.5 1 1.5 ) 2 2.5 34 Trial 2 (for fixed , this is a function of x) (function of the parameter 3 3 2 2 1 1 0 0 -0.5 0 y 0 1 x 2 Soft Computing STTS – Yosi Kristian 3 0.5 1 1.5 ) 2 2.5 35 Trial 3 (for fixed , this is a function of x) (function of the parameter 3 3 2 2 1 1 0 0 -0.5 0 y 0 1 x 2 Soft Computing STTS – Yosi Kristian 3 0.5 1 1.5 ) 2 2.5 36 Done with simplification, back to real world. Hypothesis: Parameters: Cost Function: Goal: Soft Computing STTS – Yosi Kristian 37 The Contour Figures …. How to find minimum of J in that? Soft Computing STTS – Yosi Kristian 38 Gradient Descent.. • Next Week… Soft Computing STTS – Yosi Kristian 39