02 - Overview of ML.pptx - Introduction to Machine Learning Module 2 Lecturers Dr Rafael Cabredo DLSU Maria Isabel Saludares UP Learning Outcomes 1

02 - Overview of ML.pptx - Introduction to Machine Learning...

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Introduction to Machine Learning Module 2 Lecturers: Dr. Rafael Cabredo, DLSU Maria Isabel Saludares, UP
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Learning Outcomes 1. Describe machine learning and its real-world applications; 2. Differentiate supervised and unsupervised learning techniques; 3. Demonstrate the machine learning pipeline using simple supervised and unsupervised technique; 4. Demonstrate data visualization using python libraries; 5. Identify the proper performance metrics to use; and 6. Assess the performance of models (for overfitting/underfitting).
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Artificial Intelligence Kasparov vs. Deep Blue, 1996 Jibo: Social Robot, 2016 Tesla: Self driving car, 2015
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“[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning…” (Bellman, 1978) Thinking like humans “The study of the computations that make it possible to perceive, reason, and act” (Winston, 1992) Thinking rationally “The study of how to make computers do things at which, at the moment, people are better” (Rich and Knight, 1991) Acting like humans “The branch of computer science that is concerned with the automation of intelligent behavior” (Lugger and Stubblefield, 1993) Acting rationally A lot of development! Russel and Norvig (2010) AIMA
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Building Intelligent Systems Knowledge-based systems are told what it needs to know Bicycle is a 2-wheeled vehicle with seat and handles, propelled by pedaling It looks like this: Declarative Approach Procedural Approach Machine Learning
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Building Intelligent Systems Knowledge of rules, algorithms and procedures using formal language or symbolic representations grandparent(X, Z) :- parent(X, Y), parent(Y, Z). can_fly(X) :- bird(X), has_wing(X). Machine Learning Declarative Approach Procedural Approach
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Building Intelligent Systems Programs look at data and try to find interesting patterns in it Output: facts or declarative information that knowledge- based systems can use Machine Learning Declarative Approach Procedural Approach l
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Machine Learning Field of study that gives computers the ability to learn without being explicitly programmed. [A. Samuel, 1959] 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 . [T. Mitchell, 1998]
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Problems to solve using machine learning Pattern classification Clustering / Categorization Function Approximation Prediction / Forecasting Optimization Recommendation Anomaly Detection Control and more...
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Machine Learning Pipeline Dataset Learning Algorithm Model features h θ (X) *iuqVEjdtEMY8oIu3cGwC1g.png
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Machine Learning Pipeline Dataset Learning Algorithm Model 0 500 1000 1500 2000 2500 0 100 200 300 400 Price (PhP) in 1000’s Size in meter 2 750 180 240 h θ (X)
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Machine Learning Pipeline Training set Learning Algorithm model Testing set predictions Dataset (split for training and testing)
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