Machine Learning (1)

Apr 08, 2023

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Machine learning algorithums are designed to learn from data and improve over time, without the need for manual intervention or programming

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Machine Learning (1)

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Machine Learning

Machine Learning. A Quick look. Sources: Artificial Intelligence – Russell & Norvig Artifical Intelligence - Luger. By: H é ctor Muñoz-Avila. same. Environment. Environment. System. Action 1. Action 2. Knowledge. Knowledge. changed. What Is Machine Learning?.

286 views • 12 slides

Machine Learning

Machine Learning. Lecture 3 ADLINE and Delta Rule. w 0. w 0. x 1. x 1. w 1. w 1. x 2. x 2. w n. w n. x n. x n. The ADLINE and Delta Rule. Adaptive Linear Element (ADLINE) VS Perceptron. o. o. The ADLINE and Delta Rule. Adaptive Linear Element (ADLINE) VS Perceptron

959 views • 25 slides

Machine Learning

Machine Learning. A Quick look. Sources: Artificial Intelligence – Russell & Norvig Artifical Intelligence - Luger. By: H é ctor Muñoz-Avila. Action 1. Action 2. Knowledge. Knowledge. changed. What Is Machine Learning?.

470 views • 32 slides

Machine Learning

Machine Learning. Lecture 4 Multilayer Perceptrons. w 0. w 0. x 1. x 1. w 1. w 1. x 2. x 2. w n. w n. x n. x n. Limitations of Single Layer Perceptron. Only express linear decision surfaces. y. y. Nonlinear Decision Surfaces.

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Machine Learning 1

Machine Learning 1. Genetic Algorithms. Who’s This?. Charles Darwin (1809-1882). What’s This?. HMS Beagle The Voyage of the Beagle (1839) (1831-1836). Darwin’s Finches.

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Machine Learning

Machine Learning. Lecture # 1. Contents. Why machine learning (ML) useful ? What is ML ? Key steps of learning Types of ML algorithms. Why Machine learning . Computational power is available (Resource) Recent progress in algorithms and theory (Resource)

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Machine Learning

Machine Learning. Lecture 1 Introduction. Motivating Problems. Handwritten Character Recognition. Motivating Problems. Fingerprint Recognition (e.g., border control) . Motivating Problems. Face Recognition (security access to buildings etc). Can Machines Learn to Solve These Problems?.

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Machine Learning

Machine Learning. Lecture 6 K-Nearest Neighbor Classifier. x 1. 2. 3. 1. 4. 5. 6. 7. 10. 9. 11. 12. 13. 14. 8. x 2. 15. 16. Elliptical blobs (objects). Objects, Feature Vectors, Points. X(15). X(1). X(7). X(16). X(3). X(8). X(25). X(12). X(13). X(6). X(9). X(10).

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Machine Learning

Machine Learning. Dr. Xin Chen College of Life Sciences Zhejiang University. The Instructor. Research Areas: 1. Biological Knowledge Representation & Integration 2. Machine Learning in Bioinformatics 3. Computer Aided Drug Design

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Machine Learning

Machine Learning. Definition 1 â€œThe subfield of AI concerned with programs that learn from experienceâ€ Russell / Norvig, AIMA Definition 2 â€œthe application of induction algorithms, which is one step in the knowledge discovery process.â€

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Machine Learning

Machine Learning. Decision Trees. Exercise Solutions. Exercise 1.

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MACHINE LEARNING

MACHINE LEARNING. MIS125 Uğur Güç Hazal Kalaycı Aydın Turan Yasin Can Parlak. UĞUR GÜÇ HAZAL KALAYCI AYDIN TURAN YASİN CAN PARLAK. 1. Artificial Intelligence.

3.28k views • 21 slides

Machine Learning

Machine Learning. Neuronale Netze 2 (Mitchell Kap. 4). y. 1. 0. 1. A. A. A. A. A. A. A. 0. A. A. A. h. h. A. 1. 2. A. A. B. A. B. B. A. B. A. B. B. B. B. Kombination mehrerer Neuronen. zwei Klassen, die nicht linear separierbar sind:

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Machine Learning

Machine Learning. Lecture 7 Data Clustering. Motivating Problems. A true colour image – 24bits/pixel, R – 8 bits, G – 8 bits, B – 8 bits A gif image - 8bits/pixel. 1677216 possible colours. 256 possible colours. Motivating Problems. (r, g, b). r 0. g 0. b 0. r 1. g 1. b 1.

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Machine Learning

Machine Learning. A Little Introduction Only. Introduction to Machine Learning. Decision Trees. Overfitting. Artificial Neuronal Nets. Why Machine Learning (1). Growing flood of online data Budding industry Computational power is available progress in algorithms and theory.

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Machine Learning

Machine Learning. Reading: Chapter 18. Text Classification. Is text i a finance new article?. Positive. Negative. Investors 2 Dow 2 Jones 2 Industrial 1 Average 3 Percent 5

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Machine Learning

Machine Learning. Lucy Hederman. KBS Development. Stage 1: analysis of the problem that produces a representation of the problem that can be manipulated by the reasoning system - this representation is often a set of attribute values.

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Machine Learning

Machine Learning. Berlin Chen 2006. References: 1. E. Alpaydin, Introduction to Machine Learning , Chapter 1 2. Kantard, Data Mining: Concepts, Models, Methods and Algorithms , Chapter 1 3. Mitchell, Machine Learning , Chapter 1

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Machine Learning

Machine Learning. Filename: eie426-machine-learning-0809.ppt. 2020/1/4. 1. Contents. Machine learning concepts and procedures Learning by recording cases Learning by building identification trees Simplification of decision rules. 2020/1/4. 2. Machine Learning.

410 views • 35 slides

Machine Learning

Machine Learning. Definition 1 “The subfield of AI concerned with programs that learn from experience” Russell / Norvig, AIMA Definition 2 “the application of induction algorithms, which is one step in the knowledge discovery process.”

123 views • 11 slides

Machine Learning: Lecture 1

Machine Learning: Lecture 1. Overview of Machine Learning (Based on Chapter 1 of Mitchell T.., Machine Learning , 1997). Machine Learning: A Definition.

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