and development of machine learning algorithms. We have implemented several machine learning projects in different fields in Indonesia. About Us Recent Projects • Crop Disease Prediction In March 2017, we built a machine learning algorithm to detect crops disease utilizing image recognition.
last quarter of 2017, we made a media monitoring and information extraction tool based on Natural Language Processing to identify sentiment towards specific topics. • Credit Scoring We made a Credit Scoring model based on Machine Learning Algorithm to minimize credit default and systematic risk for PT Permodalan Nasional Madani (PT PNM)
one of Sinarmas’ startup, Bizzy Indonesia. We have built several services to optimize their core logistics business. We built Vehicle Routing optimization “Truck Way”, Salesman optimization “Field Force”, Credit Scoring system and Recommendation system “Tokosmart”, Product-Toko visual recognition and build internal Machine Learning platform. About Us
was a Senior Data Engineer at Bizzy. Relevant experiences: Build marketing platform for Sampoerna, Qubicle. Worked as a developer for Mivo, Broadcast Media TV. Build decision optimization platform for Bizzy, Truck Routing. Currently, the CEO of Pacmann ai. He is an ex Research ML Scientist at Bizzy. Relevant experiences: Build Recommender System for Bizzy TokoSmart. Build Face Recognition, Person Detection, Age and Gender prediction for TokoSmart using Computer Vision. ADITYO SANJAYA RIYAD RIVANDI BADARUDDIN R MOTIK He is the COO of ML startup, Pacmann ai. He was an Independent Consultant, and had assist in creating Digital Media Solution for SME’s Relevant experiences: Co-Founder of Kitabisa.com. Work as an Ads Content Specialist at Google via Adecco.
What is Machine Learning? 4. What is Artificial Intelligence? 5. What is Data Sciences? 6. Data Science Workflow 7. Do you need Statistics, ML, or Data Sciences? 8. Machine Learning Cases in Central Bank 9. Things you need to learn to apply good Statistics and Machine Learning in Central Bank
through observation” -- OECD Statistical Definition Data Examples: All Numericals • Excel Sheet Most common understanding of data is an excel sheet. Most of the time, it is generated by business processes, or surveys, or economy activities. What is Data?
systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. - 10 terabytes of data ++ Growth of Data
used for analytics 1. Is your data bigger than 10 terabytes? No? Then don’t use BigData 2. Is your data smaller than 16 GB? Yes? Just use your laptop. 3. You can’t fit your data into your RAM? Yes? Buy more RAM, it’s cheaper Small Data
linear regression to estimate MPC as indicator of economy healthiness. Low MPC, i.e higher MPS, might indicate uncertainties in the future. What is Statistics?
high or low bias depending on whether its mean is far from or close to theta. It has high or low variance depending on whether its mass is spread out or concentrated.
gives computers the ability to learn without being explicitly programmed” ▪ Arthur Samuel (1959) Machine learning focus on accuracy, it doesn’t focus on inference or causality.
be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B... We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?”
understand it, to process it, to extract value from it, to visualize it, to communicate it's going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data.” – Hal Varian, Google Chief of Economist What is Data Science?
a computer scientist and more computer science than a statistician.” - Josh Blumenstock What is Data Science? “Data Scientist = statistician + programmer + storyteller + artist” - Shlomo Aragmon + • Machine Learning • Subject Matter Expertise
on parameter inference Focus on accuracy We do ML and Stats You need a valid conclusion You need accurate prediction We make separate model for inference and prediction Small data and noisy Unstructured data We do both noisy data and unstructured data Need subject matter expertise Does not need subject matter expertise Need subject matter expertise One time run only Predict rapidly Predict rapidly and time to time inference
also more accurate, is to train a machine learning model on a set of validated supervisory alerts which indicate the need for closer scrutiny of a particular firm.” Our first case study for supervised learning is the prediction of alerts associated with balance sheet items of financial institutions which could be reason for concerns.” Source: Machine Learning at Bank of England https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2017/machine-lear ning-at-central-banks.pdf?la=en&hash=EF5C4AC6E7D7BDC1D68A4BD865EEF3D7EE 5D7806 “Regular close scrutiny of banks’ balance sheets has become a standard for financial supervisors following the financial crises. However, the manual inspection of hundreds or thousands of firms records’ can be inefficient. Most firms will be sound and spotting complex relations between items for firms which are not, can be difficult. “ Data Science Cases
trainings in the past. Our focus is to teach a good practice of Statistics, Machine Learning, Optimization and Algorithms in industries. Last training • 500 ++ alumnis ◦ 410 alumnis with bachelor degrees ◦ 80 alumnis with master degrees ◦ 30 alumnis with doctoral degrees • 50 ++ institutions
sangat advance, yang bahkan tidak dipelajari secara umum di bangku Universitas.” -- Bimandra Djaafara, Researcher, Eijkman-Oxford Clinical Research Unit. -- PhD Student at Imperial College London
bahkan lebih bagus jauh daripada kelas machine learning kampus saya (NUS, Computer Engineering Department).” -- Prasetya Dwicahya -- Analyst, World Bank
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![Adityo Sanjaya [email protected] Thank You](https://files.speakerdeck.com/presentations/0bb772b37e75438ebc455d1acb346d27/slide_99.jpg){kind=link}