Keyword Analysis & Research: ee559


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Search Results related to ee559 on Search Engine

  • EE 559 Machine Learning I: Supervised Methods Spring 2024

    usc.edu

    https://web-app.usc.edu/soc/syllabus/20241/30490.pdf

    WEBEE 559 Machine Learning I: Supervised Methods Spring 2024. Units: 4. Lecture: MW 4:00 – 5:50 PM Pacific Time Discussion: Fri 11:00 – 11:50 AM Pacific Time Location: OHE 122 and DEN@Viterbi. Course URL: https://courses.uscden.net/d2l/home. Instructor: B. Keith Jenkins . Office: EEB 404A.

    DA: 97 PA: 57 MOZ Rank: 18

  • ClarenceCHL/USC-EE559-Machine-Learning-HW5 - GitHub

    github.com

    https://github.com/ClarenceCHL/USC-EE559-Machine-Learning-HW5

    WEBUSC-EE559-Machine-Learning-HW5. This is the HW5 of EE559 Machine Learning at the University of Southern California for the Spring 2022 semester.

    DA: 3 PA: 32 MOZ Rank: 8

  • EE559 Syllabus - University of Southern California

    usc.edu

    https://web-app.usc.edu/soc/syllabus/20232/30585.pdf

    WEBEE 559: Machine Learning I: Supervised Methods (Summer 2023) Units: Instructor: ce Location: ce Hours: TA(s): ce Hours: ce Location: Course Producer(s): Lecture(s): Discussion(s): Webpages: Prerequisites: Corequisites: Other Requirements: Tentative Grading: 4 Mohammad Reza Rajati, PhD.

    DA: 16 PA: 46 MOZ Rank: 10

  • EE 559 Machine Learning I: Supervised Methods (including …

    usc.edu

    https://web-app.usc.edu/soc/syllabus/20211/30728.pdf

    WEBThis course is intended for graduate students in Electrical and Computer Engineering or related fields, who wish to gain an understanding of, and some experience with, machine learning approaches, tools, and techniques. Only supervised-learning methods are covered in this course. Learning Objectives.

    DA: 66 PA: 9 MOZ Rank: 72

  • Deep learning - EE-559 - EPFL

    epfl.ch

    https://edu.epfl.ch/coursebook/en/deep-learning-EE-559

    WEBEE-559 / 4 credits. Teacher: Cavallaro Andrea. Language: English. Withdrawal: It is not allowed to withdraw from this subject after the registration deadline. Summary. This course explores how to design reliable discriminative and generative neural networks, the ethics of data acquisition and model deployment, as well as modern multi-modal models.

    DA: 18 PA: 93 MOZ Rank: 44

  • EE 559 Machine Learning I: Supervised Methods - Acalog ACMS™

    usc.edu

    https://catalogue.usc.edu/preview_course_nopop.php?catoid=14&coid=217371

    WEBApr 10, 2024 · EE 559 Machine Learning I: Supervised Methods. Units: 4 Terms Offered: Sp Distribution-free and probabilistic methods for supervised classification and regression; learning algorithms; optimization techniques; feature-space transformations; parametric and nonparametric methods; Bayes decision theory; artificial neural networks.

    DA: 54 PA: 41 MOZ Rank: 53

  • EE 559 - Machine Learning I: Supervised Methods - Coursicle

    coursicle.com

    https://www.coursicle.com/usc/courses/EE/559/

    WEBEE 559 at the University of Southern California (USC) in Los Angeles, California. Distribution-free and probabilistic methods for supervised classification and regression; learning algorithms; optimization techniques; feature-space transformations; parametric and nonparametric methods; Bayes decision theory; artificial neural networks.

    DA: 24 PA: 21 MOZ Rank: 4

  • GitHub - keithchugg/ee559_spring2023: EE559 Class examples …

    github.com

    https://github.com/keithchugg/ee559_spring2023

    WEBUSC EE559: EE559 Machine Learning I: Supervised Learning -- Spring 2023. This is a public repo of examples used in this class taught by Keith Chugg and B. Keith Jenkins.

    DA: 73 PA: 3 MOZ Rank: 13

  • tej-88/USC-EE559-HW01 - GitHub

    github.com

    https://github.com/tej-88/USC-EE559-HW01

    WEBClass: EE-559. Date: 14-06-2023. Assignment: Homework 1. OS: Linux Mint. Distribution: Anaconda. Synthetic-1. Model: Nearest Means Classifier. Training Error Rate: 21.00% …

    DA: 27 PA: 75 MOZ Rank: 46

  • EE 559 Syllabus S19 v0-9 - University of Southern California

    usc.edu

    https://web-app.usc.edu/soc/syllabus/20191/30561.pdf

    WEBEE 559 Mathematical Pattern Recognition Spring 2019 EE 559 Course Syllabus Page 3 of 10 Contact Information Instructor: Prof. B. Keith Jenkins, EEB 404A Email: [email protected] (please include “EE 559" in the

    DA: 26 PA: 95 MOZ Rank: 74