Machine Learning (AIMS) - MT 2018 0. (My) Introduction to ML...Machine Learning (AIMS) - MT 2018 0....

Machine Learning (AIMS) - MT 2018

0. (My) Introduction to ML

Varun Kanade

University of OxfordNovember 5, 2018

Machine Learning in Action

(Using https://www.betafaceapi.com/demo.html)

What is machine learning?

circa October 2016

circa October 2017

What is artificial intelligence?

‘‘Instead of trying to produce a programme tosimulate the adult mind, why not rather try toproduce one which simulates the child’s? If this werethen subjected to an appropriate course of educationone would obtain the adult brain.’’

Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59, 433-460.

What is artificial intelligence?

‘‘Instead of trying to produce a programme tosimulate the adult mind, why not rather try toproduce one which simulates the child’s? If this werethen subjected to an appropriate course of educationone would obtain the adult brain.’’

Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59, 433-460.

Definition by TomMitchell

A computer program is said to learn from experience E with respect tosome class of tasks T and performance measure P, if its performance attasks in T, as measured by P, improves with experience E.

Face Detection

I E : images (with bounding boxes) around faces

I T : given an image without boxes, put boxes around faces

I P : number of faces correctly identified

Face Detection

Outline

Course Logistics

Some Machine Learning Applications

Course Information

Websitewww.cs.ox.ac.uk/people/varun.kanade/teaching/ML-AIMS-MT2018/

LecturesMon-Thu: 10h30 - 12h30; Fri: 9h-11h (Lecture Room 7 all days)

PracticalsMon-Thu: 15h-17h (?)

Demonstrators: Philip Lazos, David Martínez

TextbooksKevin Murphy - Machine Learning: A Probabilistic PerspectiveI Online access through Bodleian library

Other posted online material

A few last remarks about this course

4! As ML developed through various disciplines - CS, Stats,Neuroscience, Engineering, etc., there is no consistent usageof notation or even names among the textbooks. At timesyou may find inconsistencies even within a single textbook.

You will be required to read, both before and after the lectures. I will postsuggested reading on the website.

Resources:

I Wikipedia has many great articles about ML and background material

I Online videos: Andrew Ng on coursera, Nando de Freitas on youtube, etc.

I Many interesting blogs, podcasts, etc.

NIPS Papers!

Advances in Neural Information Processing Systems 1988

NIPS Papers!

Advances in Neural Information Processing Systems 2017 [video]

Outline

Course Logistics

Some Machine Learning Applications

Application: Boston Housing Dataset

Numerical attributes

I Crime rate per capita

I Non-retail business fraction

I Nitric Oxide concentration

I Age of house

I Floor area

I Distance to city centre

I Number of rooms

Categorical attributes

I On the Charles river?

I Index of highway access (1-5)

Predict house cost

Source: UCI repository

Application: Object Detection and Localisation

I 200-basic level categories

I Here: Six pictures containing airplanes and people

I Dataset contains over 400,000 images

I Imagenet competition (2010-)

I All recent successes through very deep neural networks!

Supervised Learning

Training data has inputs x (numerical, categorical) as well as outputs y(target)

Regression: When the output is real-valued, e.g., housing price

Classification: Output is a category

I Binary classification: only two classes e.g., spam

I Multi-class classification: several classes e.g., object detection

Unsupervised Learning : Genetic Data of European Populations

Source: Novembre et al., Nature (2008)

Experience (E)

Task (T)

Performance (P)

Dimensionality reduction - Map high-dimensional data to low dimensions

Clustering - group together individuals with similar genomes

Experience (E)

Task (T)

Performance (P)

Experience (E)

Task (T)

Performance (P)

Unsupervised Learning : Group Similar News Articles

Group similar articles into categories such as politics, music, sport, etc.

In the dataset, there are no labels for the articles

Active and Semi-Supervised Learning

Active Learning

I Initially all data is unlabelled

I Learning algorithm can ask a human to labelsome data

Semi-supervised Learning

I Limited labelled data, lots of unlabelled data

I How to use the two together to improvelearning?

Collaborative Filtering : Recommender Systems

Movie / User Alice Bob Charlie Dean EveThe Shawshank Redemption 7 9 9 5 2

The Godfather 3 ? 10 4 3The Dark Knight 5 9 ? 6 ?Pulp Fiction ? 5 ? ? 10

Schindler’s List ? 6 ? 9 ?

Netflix competition to predict user-ratings (2008-09)

Any individual user will not have used most products

Most products will have been use by some individual

Reinforcement Learning

I Automatic flying helicopter; self-driving cars

I Cannot conceivably program by hand

I Uncertain (stochastic) environment

I Must take sequential decisions

I Can define reward functions

I Fun: Playing Atari breakout! [video]

Machine Learning (AIMS) - MT 2018 0. (My) Introduction to ML...Machine Learning (AIMS) - MT 2018 0....

Documents

Perkongsian Unit Bahasa Melayu Darjah 2changkatpri.moe.edu.sg/qql/slot/u534/Parents/2016/MT/ML/ML P2... · Program Bacaan Sepanjang Tahun • Setiap Selasa jam 2.30ptg – 3.30ptg

qaþ/20 MT MT MT MT (#Y7JL) AU/20 TEL:058-267-0020 FAX:058 … · 058-267-0031 MT MT MT MT MT MT MT MT MT MT MT MT CEBa—YaSY MT (#Y7Jb) MT \qaþ/20 MT MT MT MT (#Y7JL) AU/20 TEL:058-267-0020

Reer Catalog 11 - BIBUS · PDF fileAN SI / UL 1998 “ af e ty So w rin P og amm bl C mpo n s ... - Janus ML and ML TRX and Janus MT and MT TRX featuring an innovative horizontal arm

Visiting Student Modules Trinity Module Name Credits ... Studies... · Term half-module 10 MT & HT Aims The main learning aims of this module are to enable students to develop competence

Krishlar Pharma · 1 amp,lml 1 amp,lml 3x2 ml Ix5 ml 30 ml 30 ml 30 ml 30 ml 30 ml 30 ml 30 ml 60 ml 60 ml 60 ml 60 ml 60 ml 60 ml 60 ml 30 ml 1.5 gmx20 5 gmxIO 30 gm ... KRITMOX-CV

santral.com · MT 058 MT 059 MT 060 MT 055 MT 079 MT 070 MT 071 MT 072 MT 073 MT 074 MT 075 MT 028 MT 056 MT 1094 MT 1095 Cinsi Bir fazll a kapama alter iki fazll aqp ka ama ¥lter

Mt hermon. mt carmel mt horeb mt hermon mt gerizim mt hebron

NTEGROVANDÝ OPRASVYN SÍTÉMO LOMOUCKÉHOK RAJE … · ml ml ml ml ml ml ml ml ml ml ml ml ml ml ml ml ml ml m ml ml ml ml lm ml ml ml ml lm ml ml ml ml ml ml lm ml ml ml lm ml lm

Sin título-1 - Bimeda · PRESENTACIÓN CAPRINOSL 500 ml 500 ml 500 ml 500 ml 50 ml 500 ml IL IL 6.4 g 23.6 g 100 ml 100 ml 250 ml 250 ml 475 ml 100 ml 100 ml 250 ml 500 ml 240 ml

MT-06 WINE MT-22 COBALT MT-33 MASTIC MT-43 …MT-06 WINE MT-22 COBALT MT-33 MASTIC MT-43 GRAY MT-09 RED MT-24 IRON BLUE MT-36 PINK MT-44 CHARCOAL MT-12 ORANGE MT-25 INDIGO MT-37 BERRY

HAGLEITNER fertőtlenítési KISOKOS · 3 Liter 8 ml 15 ml 30 ml 45 ml 60 ml 75 ml 120 ml 150 ml 210 ml 225 ml 4 Liter 10 ml 20 ml 40 ml 60 ml 80 ml 100 ml 160 ml 200 ml 280 ml 300

MI TRX / MI TRX L / ML TRX / MT TRX / J TRXL · 8540697 • 16/11/2012 • Rev.12 3 PRINCIPLE OF OPERATION Figure 1 shows the MI/ML/MT TRX way of operation : The active element M

CICAN - FEVEREIRO 2018pppimagembahia.com.br/wp-content/uploads/2017/07/21...2017/07/21 · ARILMA DE JESUS SANTOS 8 10:00 às 19:00 MT MT MT MT MT MT MT MT MT MT MT MT MT MT MT MT

MI TRX / MI TRX L / ML TRX / MT TRX / J TRXL...8540697 • 07/04/2009 • Rev.7 3 PRINCIPLE OF OPERATION Figure 1 shows the MI/ML/MT TRX way of operation : The active element M TRX

Grow Micro Bloom Honey · Week 7 Late Flowering - 6.0 mL 11.0 mL 10.0 mL 8.0 mL 8.0 mL - Week 8 Ripening - 4.0 mL 11.0 mL 5.0 mL 4.0 mL 6.0 mL - Week 9 Flush 2.0 mL 2.0 mL 2.0 mL

SERIE SAGA - puertastht.com¡l… · L1150 MT L1130 MT L1120 MT Prov MT Semi MT Coral MT Yedra MT Clas MT 1110 MT 1150 MT 1150CL MT. Title: CATTALOGO 2016.ai Author: MARIA MARTIN

D.1 - Tlaxcala · ml. ml ml mt. mt nt. ejec. 230.40 64 91.8 173.80 162.68 fism-1633026 fism-1633028 fism-1633056 fism-1633074 proagua-ooi inicial 1633009 modificado ffm.1633014 ism-1633052

16 Januari 2016 Sekolah Rendah Changkatchangkatpri.moe.edu.sg/qql/slot/u534/Parents/2016/MT/ML/ML P3... · Program yang dijalankan ... Aktiviti Kelas •Sepak raga, congkak, lagu

MI TRX / MI TRX L / ML TRX / MT TRX / J TRX L - reer.it file 8540697 • 16/11/2012 • rev.12 1 barrera fotoelÉctrica de seguridad de tipo 4 mi trx / mi trx l / ml trx / mt trx

MI TRX / MI TRX L / ML TRX / MT TRX / J TRXL - reer.it · 8540697 • 16/11/2012 • rev.12 1 barriera fotoelettrica di sicurezza di tipo 4 mi trx / mi trx l / ml trx / mt trx / j