PENGANTAR MACHINE LEARNING (Pra Kuliah Umum)

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 ilmu
dari Computer Science yang mempelajari
bagaimana membuat komputer/mesin itu
mempunyai suatu kecerdasan. Agar mempunyai
suatu kecerdasan, komputer/mesin harus dapat
belajar.
Machine Learning
• Machine Learning adalah suatu bidang
keilmuan yang berisi tentang pembelajaran
komputer/mesin untuk menjadi cerdas
Traditional Programming
VS Machine Learning
Traditional Programming
Data
Program
Computer
Output
Computer
Program
Machine Learning
Data
Output
Magic?
No, more like gardening
•
•
•
•
Seeds = Algorithms
Nutrients = Data
Gardener = You
Plants = Programs
Sekilas tentang
Machine Learning
A Few Quotes
• “A breakthrough in machine learning would be worth
ten 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)
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/
9
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
• ...
10
The steps needed for any model-based machine
learning application:
Sumber : http://www.mbmlbook.com/LifeCycle.html
Contoh Manfaat Machine Learning
….dst
Topik pembahasan dalam
Machine 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 dalam
Machine 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 dalam
Machine 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 dalam
Machine Learning (4)
• Learning Representation
– PCA
- ICA
- Fisher Linear Discrimination
• Kernel Methods and SVM
– Kernels
– Maximizing Margin
- SVMs
-Noise and Soft Margin
Topik pembahasan dalam
Machine Learning (5)
• Deep Learning
– Early Work
– Stacked Auto Decoders
- Why Deep Learning
- Deep Belief Networks
Referensi
• Slide CSE 446 Machine Learning oleh Pedro
Domingos
• Slide Introduction to Machine Learning oleh Entin
Martiana (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 unsur
plagiat (copy paste) maka nilai dibagi dengan
hasil 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 [email protected]
• 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 penalti
dikurangi 1 poin/kesalahan
• Kesalahan format file maka tidak dikoreksi
• Pertemuan 10
KULIAH UMUM
MACHINE
LEARNING
Selasa,4 April 2017
di Aula UNSIKA