PPT-6.S093 Visual Recognition through Machine Learning Competit

Author : trish-goza | Published Date : 2016-05-02

Image by kirkhdeviantartcom Aditya Khosla and Joseph Lim Todays class Part 1 Introduction to deep learning What is deep learning Why deep learning Some common

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6.S093 Visual Recognition through Machine Learning Competit: Transcript

Image by kirkhdeviantartcom Aditya Khosla and Joseph Lim Todays class Part 1 Introduction to deep learning What is deep learning Why deep learning Some common deep learning algorithms. Chapter . 2: Perception (Part II). also see: neurological structures.pdf. also see: Kellogg chapter 2 (part I).. pdf. Fund. . of Cognitive . Psychology (2. nd. ) . (Kellogg). Fall . 2013. Mark Van Selst. Several slides from . Luke . Xettlemoyer. , . Carlos . Guestrin. and Ben . Taskar. Typical Paradigms of Recognition. Feature Computation. Model. Visual Recognition. Identification. Is this your car?. wearable accelerometers. Mitja Luštrek. Jožef Stefan Institute. Department of Intelligent Systems. Slovenia. Tutorial at the University of Bremen, November 2012. Outline. Accelerometers. Activity recognition with machine learning. Recognition tasks. Machine learning approach: training, testing, generalization. Example classifiers. Nearest neighbor. Linear classifiers. Image features. Spatial support:. Pixel or local patch. Segmentation region. 1. Speech Recognition and HMM Learning. Overview of speech recognition approaches. Standard Bayesian Model. Features. Acoustic Model Approaches. Language Model. Decoder. Issues. Hidden Markov Models. David Kauchak. CS 451 – Fall 2013. Why are you here?. What is Machine Learning?. Why are you taking this course?. What topics would you like to see covered?. Machine Learning is…. Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.. E . Wolf decoy for geese (plenty in New England).. Why are these decoys efficient?. Geese do not have pictures to teach their young ones what animals to avoid…. … and so they must be born with images of predators engraved in their brain. Larry Zitnick. Facebook AI Research. 1984. Neocognitron. , 1983. Recognition?. 1984. 2016. Data. GPUs. Backprop. Neocognitron. , 1983. AlexNet. , 2012. Recognition. 1984. 2016. Data. GPUs. Backprop. Recognition. CS539. Prof. Carolina Ruiz. Department of Computer Science . (CS). & Bioinformatics and Computational Biology (BCB) Program. & Data Science (DS) Program. WPI. Most figures and images in this presentation were obtained from Google Images. The inability to recognise familiar objects presented visually is known as visual . agnosia. . There are two main types:. Apperceptive. . agnosia. – a failure to recognise due to impaired visual perception. This implies a physiological problem in the visual system.. wearable accelerometers. Mitja Luštrek. Jožef Stefan Institute. Department of Intelligent Systems. Slovenia. Tutorial at the University of Bremen, November 2012. Outline. Accelerometers. Activity recognition with machine learning. also see: neurological structures.pdf. also see: Kellogg chapter 2 (part I).. pdf. Fund. . of Cognitive . Psychology (2. nd. ) . (Kellogg). Fall . 2013. Mark Van Selst. San Jose State University. Assignment 2: Neuroscience (5%). (CS725). Autumn 2011. Instructor: . Prof. . Ganesh. . Ramakrishnan. TAs: . Ajay Nagesh, Amrita . Saha. , . Kedharnath. . Narahari. The grand goal. From the movie . 2001: A Space Odyssey. (1968). Outline. Pengtao. . Xie. 3/5/2014. 1. Outline. Gaussian Mixture Model. Expectation Maximization. 3/5/2014. 2. Motivation. 3/5/2014. 3. Formulation. 3/5/2014. 4. K clusters. GMM is a distribution. 3/5/2014. 5.

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