PENGANTAR MACHINE LEARNING(Pra Kuliah Umum)

Betha Nurina Sari,M.Kom

Machine Learning

• Machine Learning is the study of computer algorithms that improve automatically through experience. Applications range from data mining programs that discover general rules in large data sets, to information filtering systems that automatically learn users' interests (Tom Mitchell, 1997).

Machine Learning

• Machine Learning adalah salah satu disiplin ilmudari Computer Science yang mempelajaribagaimana membuat komputer/mesin itumempunyai suatu kecerdasan. Agar mempunyaisuatu kecerdasan, komputer/mesin harus dapatbelajar.

Machine Learning

• Machine Learning adalah suatu bidangkeilmuan yang berisi tentang pembelajarankomputer/mesin untuk menjadi cerdas

Traditional Programming

Machine Learning

ComputerData

ProgramOutput

ComputerData

Output

Program

Traditional Programming VS Machine Learning

Magic? No, more like gardening

• Seeds = Algorithms

• Nutrients = Data

• Gardener = You

• Plants = Programs

Sekilas tentangMachine Learning

A Few Quotes

• “A breakthrough in machine learning would be worthten Microsofts” (Bill Gates, Chairman, Microsoft)

• “Machine learning is the next Internet” (Tony Tether, Director, DARPA)

• Machine learning is the hot new thing” (John Hennessy, President, Stanford)

• “Web rankings today are mostly a matter of machine learning” (Prabhakar Raghavan, Dir. Research, Yahoo)

• “Machine learning is going to result in a real revolution” (Greg Papadopoulos, CTO, Sun)

• “Machine learning is today’s discontinuity” (Jerry Yang, CEO, Yahoo)

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Resources: Datasets

• UCI Repository: http://www.ics.uci.edu/~mlearn/MLRepository.html

• UCI KDD Archive: http://kdd.ics.uci.edu/summary.data.application.html

• Statlib: http://lib.stat.cmu.edu/

• Delve: http://www.cs.utoronto.ca/~delve/

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Resources: Journals

• Journal of Machine Learning Research www.jmlr.org

• Machine Learning

• IEEE Transactions on Neural Networks

• IEEE Transactions on Pattern Analysis and Machine Intelligence

• Annals of Statistics

• Journal of the American Statistical Association

• ...

The steps needed for any model-based machine learning application:

Sumber : http://www.mbmlbook.com/LifeCycle.html

Contoh Manfaat Machine Learning

….dst

Topik pembahasan dalamMachine Learning (1)

• Probability and Estimation – Bayes rule

– Maximum Likelihood Estimation

– Max a posteriori

– Naive bayes

• Gaussian Naive Bayes and Logistic Regression – Gaussian Bayes Classifier

– Document Classification

– Form of Decision Surfaces

– Linear Regression

Topik pembahasan dalamMachine Learning (2)

• Graphical Models

– Bayes Nets

– Conditional Independence

– D-Separation and Conditional Indepence

– Inference

– Learning from fully observed data

– Learning from partially observed data

– EM algorithm

– Mixture of Gaussian Clustering

Topik pembahasan dalamMachine Learning (3)

• Hidden Markov Model

– Markov models

– HMM and Bayes Net

– Other probabilistic time series

• Artificial Neural Networks

– Non-Linear Regression

– Backpropagation and Gradient Descent

– Learning Hidden Layer Representation

Topik pembahasan dalamMachine Learning (4)

• Learning Representation

– PCA - ICA - Fisher Linear Discrimination

• Kernel Methods and SVM

– Kernels - SVMs

– Maximizing Margin -Noise and Soft Margin

Topik pembahasan dalamMachine Learning (5)

• Deep Learning

– Early Work - Why Deep Learning

– Stacked Auto Decoders - Deep Belief Networks

Referensi

• Slide CSE 446 Machine Learning oleh Pedro Domingos

• Slide Introduction to Machine Learning oleh EntinMartiana (2013)

• BRP Kuliah Pembelajaran Mesin oleh Ito Wasito(2015)

TUGAS KULIAH UMUM : RESUME

• TUGAS RESUME untuk semua mahasiswa yang mengambil mata kuliah sistem pakar, baik yang hadir atau tidak hadir kuliah umum.

• Tugas bersifat individu, kalau ditemukan unsurplagiat (copy paste) maka nilai dibagi denganhasil tugas yang sama.

• Resume berupa ringkasan atas materi yang disampaikan tentang Machine Learning

TUGAS KULIAH UMUM : RESUME

• Resume maksimal 1 halaman A4, format softfile(.pdf) dikirim paling lambat Jumat, 7 April 2017 23.59 WIB dengan subyek RESUME_NAMA melalui email ke betha.nurina@staff.unsika.ac.id

• Komponen yang harus ada :

– Identitas mahasiswa (NPM,Nama,Kelas Asal) (5 poin)

– Judul/topik resume (5 poin)

– Hasil Resume (90 poin)

• Ringkasan materi padat, jelas, ringkas

• Inspirasi/motivasi yang didapatkan

TUGAS KULIAH UMUM : RESUME

• Keterlambatan pengumpulan tugas per-hari, penalti dikurangi 10 poin

• Kesalahan penulisan dalam tugas penaltidikurangi 1 poin/kesalahan

• Kesalahan format file maka tidak dikoreksi

• Pertemuan 10

KULIAH UMUM

MACHINE LEARNING

Selasa,4 April 2017

di Aula UNSIKA