CSC 411: Machine Learning in Action Challenge : Movie Rating and - PowerPoint PPT Presentation

Jun 19, 2023 •391 likes •459 views

CSC 411: Machine Learning in Action Challenge : Movie Rating and Genre Prediction Sanja Fidler University of Toronto Jan 22, 2015 Fidler (UofT) CSC 411: Challenge Jan 22, 2015 1 / 5 Hands-On Experience A good (and fun!) way to learn the

CSC 411: Machine Learning in Action Challenge : Movie Rating and Genre Prediction Sanja Fidler University of Toronto Jan 22, 2015 Fidler (UofT) CSC 411: Challenge Jan 22, 2015 1 / 5
Hands-On Experience A good (and fun!) way to learn the concepts we are talking about is to implement them and try them on real data. We prepared movie data: http://www.cs.toronto.edu/~fidler/teaching/2015/slides/CSC411/movies.zip Try to predict ratings and genres ! Report performance on piazza Questions: ◮ What would be good features? ◮ How does each method perform? ◮ What happens if you use less vs more training data? Which method did better? ◮ What happens if your features are low or high dimensional? Which method did better? ◮ How did you choose your hyper-parameters? Note: This is not an assignment! Fidler (UofT) CSC 411: Challenge Jan 22, 2015 2 / 5
Putting Machine Learning to Practice Good advice by Andrew Ng for practical ML: http://cs229.stanford.edu/materials/ML-advice.pdf In video: https://www.youtube.com/watch?v=TxJe4xeDI7g&feature=relmfu Pedro Domingo’s paper: https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf Fidler (UofT) CSC 411: Challenge Jan 22, 2015 3 / 5
Data The data has: ◮ Train: 700 movies ◮ Test: 300 movies You may want to split the training set into train and validation Do not use test data for training or parameter tuning Each movie has lots of available meta-data, e.g.: ◮ Cast ◮ Director(s) ◮ Writer(s) ◮ Year of release ◮ Storyline (short description of the movie) ◮ Plot (longer description of the movie) ◮ Box-office information (try not using this for rating prediction) ◮ Keywords (try not using this for genre prediction) Report performance on test data Fidler (UofT) CSC 411: Challenge Jan 22, 2015 4 / 5
Kung Fu Panda This movie will be released on Jan 29 (next Friday): http://www.cs.toronto.edu/~fidler/teaching/2015/slides/CSC411/panda.mat Whose method can best guess what the rating will be? We will revisit this question in the end of Feb (when enough votes come in) Don’t cheat: by e.g., going to watch the movie and giving it a score! Fidler (UofT) CSC 411: Challenge Jan 22, 2015 5 / 5
Kung Fu Panda This movie will be released on Jan 29 (next Friday): http://www.cs.toronto.edu/~fidler/teaching/2015/slides/CSC411/panda.mat Can you accurately predict its genres? Fidler (UofT) CSC 411: Challenge Jan 22, 2015 5 / 5

Recommend

FlickOh : Personalized Movie Recommendation and Rating System What is FlickOh? Movie rating

Natth Bejraburnin Naehee Kim Seongtaek Lim Mentor: Brian Guarraci FlickOh : Personalized Movie Recommendation and Rating System What is FlickOh? Movie rating and recommendation system based on Twitter data Provide general

291 views • 11 slides

CS 4518 Mobile and Ubiquitous Computing Lecture 20: Movie Rating Emmanuel Agu Your Reaction

CS 4518 Mobile and Ubiquitous Computing Lecture 20: Movie Rating Emmanuel Agu Your Reaction Shows You Liked the Movie The Problem: Rating Movies & Videos Your reactions suggest you liked the movie: Automatic content rating via reaction

892 views • 11 slides

Higgs Machine Learning Challenge experience. A HEP pattern recognition challenge ? David

Higgs Machine Learning Challenge experience. A HEP pattern recognition challenge ? David Rousseau LAL-Orsay 10th February 2015 CTD 2015, Berkeley Outline q Machine Learning, Challenges q The Higgs Machine Learning challenge q A

738 views • 57 slides

Reinforcement Learning for Continuous State and Action Spaces Gradient Methods 1 MACHINE LEARNING

MACHINE LEARNING 2012 MACHINE LEARNING TECHNIQUES AND APPLICATIONS Reinforcement Learning for Continuous State and Action Spaces Gradient Methods 1 MACHINE LEARNING 2012 Reinforcement Learning (RL) Supervised Learning Unsupervised

777 views • 51 slides

Machine Learning (AIMS) - MT 2017 0. (My) Introduction to ML Varun Kanade University of Oxford

Machine Learning (AIMS) - MT 2017 0. (My) Introduction to ML Varun Kanade University of Oxford November 6, 2017 Machine Learning in Action (Using https://www.betafaceapi.com/demo.html) 1 Machine Learning in Action (Using

416 views • 27 slides

What is Action Learning? What is an ALC? 1 What is Action Learning? Learning by doing

What is Action Learning? What is an ALC? 1 What is Action Learning? Learning by doing Process for bringing together a group of people with varied levels of skills and experience (but with influence, passion, etc. to impact an

191 views • 5 slides

Machine Learning - MT 2016 1. Introduction Varun Kanade University of Oxford October 10, 2016

Machine Learning - MT 2016 1. Introduction Varun Kanade University of Oxford October 10, 2016 Machine Learning in Action 1 Machine Learning in Action 1 Machine Learning in Action 1 Is anything wrong? 2 Is anything wrong? (See Guardian

525 views • 50 slides

The Challenge HPC IT departments required to host Data Science and Machine Learning a

The Challenge HPC IT departments required to host Data Science and Machine Learning a variety of different workloads General applications supporting business processes Containerized and non-containerized Multiple ways

652 views • 29 slides

An Exercise in An Exercise in Machine Learning Machine Learning

An Exercise in An Exercise in Machine Learning Machine Learning http://www.cs.iastate.edu/~cs573x/bbsilab.html Machine Learning Software Preparing Data Building Classifiers Interpreting Results Machine Learning Software

496 views • 26 slides

Euro 2016 Predictions Using Team Rating Systems Jan Lasek deepsense.io Machine Learning and

Euro 2016 Predictions Using Team Rating Systems Jan Lasek deepsense.io Machine Learning and Data Mining for Sports Analytics Workshop at ECML PKDD 2016 Jan Lasek (deepsense.io) Euro 2016 Predictions MLSA at ECML PKDD16 1 / 14

575 views • 23 slides

MICROSOFT AZURE MACHINE LEARNING Oscar Naim Microsoft Microsoft Azure Machine Learning What is

MICROSOFT AZURE MACHINE LEARNING Oscar Naim Microsoft Microsoft Azure Machine Learning What is Machine Learning? Azure Machine Learning: How it works Azure Machine Learning in action Get started Contents What is Machine Learning?

456 views • 21 slides

Foundations of Machine Learning Reinforcement Learning Reinforcement Learning Agent exploring

Foundations of Machine Learning Reinforcement Learning Reinforcement Learning Agent exploring environment. Interactions with environment: action state Agent Environment reward Problem: find action policy that maximizes cumulative reward

828 views • 66 slides

Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by

Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by which computers can be trained through observation, rather than being explicitly programmed. Basically, a program is given input and adjusts its

559 views • 17 slides

Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 12, 2015 Today: Readings: What is machine learning? The Discipline of ML Decision tree learning Mitchell, Chapter 3

872 views • 29 slides

Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

GCT634/AI613: Musical Applications of Machine Learning (Fall 2020) Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning Pipeline in Classification Tasks A set of hand-designed audio features are selected

401 views • 26 slides

Sustainability Rating Tools for Existing Neighborhoods Rating tools overview 3 tools -

S ustainability R ating T ools for Existing Neighborhoods March 11, 2015 Eliot Allen, LEED AP-ND , TransformativeTools .org Sustainability Rating Tools for Existing Neighborhoods Rating tools overview 3 tools - Living Community Challenge

700 views • 30 slides

Summary of the 2015 NIST Language Recognition i-Vector Machine Learning Challenge Craig Greenberg

Summary of the 2015 NIST Language Recognition i-Vector Machine Learning Challenge Craig Greenberg Audrey Tong, Alvin Martin, George Doddington National Institute of Standards and Technology Douglas Reynolds, Elliot Singer MIT Lincoln

609 views • 19 slides

Explaining Complex Energy Systems A Challenge Tackeling Climate Change with Machine Learning

Explaining Complex Energy Systems A Challenge Tackeling Climate Change with Machine Learning 11.12.2020 NeurIPS 2020 Workshop This work was funded by German Federal Ministry of Education and Research (Bundesministerium fr Bildung und

213 views • 7 slides

Conclusions Larry Holder CptS 570 Machine Learning School of Electrical Engineering and

Conclusions Larry Holder CptS 570 Machine Learning School of Electrical Engineering and Computer Science Washington State University 1 Outline Overview of machine learning Fundamental research issues Grand challenge problems 2

549 views • 18 slides

CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine

1/22/19 CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine Learning? A Simple Task: Recognize Obama How do you program a computer to Recognize faces? Recommend movies? Decide which web

582 views • 5 slides

Machine Learning Machine Learning: algorithms that use experience to improve their

Machine Learning Machine Learning: algorithms that use experience to improve their performance We use machine learning in situations where it is very challenging (or impossible) to define the rules by hand: e.g. face detection

1.04k views • 28 slides

Machine Learning Matched Action Parameters Phiala Shanahan Motivation: ML for LQCD

Machine Learning Matched Action Parameters Phiala Shanahan Motivation: ML for LQCD First-principles nuclear physics beyond A=4 How finely tuned is the emergence of nuclear structure in nature? Interpretation of intensity-frontier

858 views • 27 slides

Challenge 1: Proposed Rating Change Option 2 seems to present less of an immediate shock to

[ SLIDE 1 ] Hello your worship the mayor and councillors, Thank you for the opportunity to provide my feedback on the proposed 10-Year Plan. Being a transport-oriented organisation, Cycle Action Waikatos principal concern is mobility. The

361 views • 5 slides

Introduction to Machine Learning COMPSCI 371D Machine Learning COMPSCI 371D Machine

Introduction to Machine Learning COMPSCI 371D Machine Learning COMPSCI 371D Machine Learning Introduction to Machine Learning 1 / 12 Outline 1 Nearest Neighbor Prediction 2 Complexity Considerations 3 The Voronoi Diagram 4 Overfitting

565 views • 12 slides