Uploaded bysrinivasanvenkat10
PDF, PPTX35 views

REvit training

This document provides an introduction to machine learning for building design and construction. It discusses key machine learning concepts such as algorithms, types, and processes. Specifically, it covers decision trees and artificial neural networks, providing examples of how each can be applied to problems in architecture, engineering, and construction. It also includes exercises for attendees to practice applying machine learning techniques.

Embed presentation

Download as PDF, PPTX
Join the conversation #AU2017 Join the conversation #AU2017 Introduction to Machine Learning for Building Design and Construction Mehdi Nourbakhsh, Ph.D. Sr. Research Scientist, Autodesk Research mehdi.nourbakhsh [at] Autodesk.com
 What is AI/ML?  History of ML  Application of ML in AEC  ML Glossary  ML Types  ML Process  ML Algorithms  Decision Tree  Artificial Neural Network  Wrap up Contents:
What is AI/ML?
History of AI/ML A lot more here
Day-to-day ML Applications
 Demo Application of ML in AEC
 Data, sample  Variables, attributes, features  Feature vector  Input, output ML Glossary outlook temperature humidity windy construction Day 1 overcast hot high FALSE yes Day 2 rainy cool normal TRUE yes Day 3 sunny mild high FALSE no
ML Glossary Feature Vector= (overcast, hot, high, FALSE)
What is the goal?
ML Glossary Model Outlook Temperature Humidity Windy Construction Input Output or label F(Outlook, Temperature, Humidity, Windy) = Ŷ
A1 A2 A3 Y 1 0.2 Yes 0.32 5 0.56 No 0.58 2 0.56 Yes 0.23 3 0.6 Yes 0.39 Exercise 1 In the following example, what is/are: • Samples • Features • Input • output
ML Types
ML Types
ML Types
ML Types
A1 A2 A3 Y 1 0.2 Yes 0.32 5 0.56 No 0.58 2 0.56 Yes 0.23 3 0.6 Yes 0.39 Exercise 1.5 Is this a supervised or unsupervised learning? Is this a classification or regression problem?
 Donten in a company that has more than 10,000 building models in their database. These models are either residential, office space, or warehouses. They want to categorize their models into these three groups and seek your recommendation as a thought-leader in the industry. Which one do you recommend?  Hiring 20 people to manually open all of the models and categorize them in 10 months?  Using machine learning to automatically categorize the models in a month? (why does it take a month?) Exercise 2
 Which type of ML is this?  How should we describe this problem to the machine?  Data?  Features?  Input, output? Exercise 2 Model ? ? … ? ? Input Output Residential Warehouse Office
Simplified ML process In practice, this is not a straightforward process, there are lots of back and forth between the steps.
ML Algorithms: Decision Tree Predict if construction will happen?
Exercise 3: Decision Tree Take a guess…
Exercise 3: Decision Tree Follow this algorithm: • Divide into subsets • Are they pure? (all ‘yes’ or ‘no’) • Yes->done • No-> repeat
Exercise 3: Decision Tree 9 Yes / 4 No Follow this algorithm : • Divide into subsets • Are they pure? (all ‘yes’ or ‘no’) • Yes->done • No-> repeat
Exercise 3: Decision Tree 9 Yes / 4 No Follow this algorithm : • Divide into subsets • Are they pure? (all ‘yes’ or ‘no’) • Yes->done • No-> repeat
Exercise 3: Decision Tree
Exercise 3: Decision Tree
Exercise 3: Decision Tree
Exercise 3: Decision Tree
Exercise 3: Decision Tree
Demo in Weka
Section break (5 min)
ML Algorithms: Artificial Neural Network Source: https://www.intechopen.com/source/html/39067/media/image1.png A) human neuron; B) artificial neuron; C) biological synapse; D) ANN synapses
 A construction company completed three projects. The estimated and actual construction cost of each project is presented in the following table. Your task is to design a cost model for the company based on the available data and predict how much is the actual cost of ‘proj 4’. Exercise 4: Construction Cost Model Estimated Construction Cost Actual Construction Cost proj 1 350 420 proj 2 200 250 proj 3 500 700 Proj 4 400 ?
 A construction company completed three projects. The estimated and actual construction cost of each project is presented in the following table. Your task is to design a cost model for the company based on the available data. Exercise 4: Construction Cost Model Input (X) Output (Y) * W Estimated Construction Cost Actual Construction Cost proj 1 350 420 proj 2 200 250 proj 3 500 700 Proj 4 400 ?
 One way of doing it:  A= Sum all estimated costs  B = Sum all actual costs  W = B/A Exercise 4: Construction Cost Model Estimated Construction Cost Actual Construction Cost proj 1 350 420 proj 2 200 250 proj 3 500 700 Sum 1050 1370 B/A 1.30 Is it good? Why not 1.5 or 2.0? Y=1.30X
 Measure of goodness = less error  What is the error of the model? Exercise 4: Construction Cost Model Estimated Construction Cost Actual Construction Cost Predicted Cost (y=1.3x) Error proj 1 350 420 455 ? proj 2 200 250 260 ? proj 3 500 700 650 ?
Exercise 4: Construction Cost Model Absolute error = | predicted cost – actual cost | Estimated Construction Cost Actual Construction Cost Predicted Cost (y=1.3x) Error proj 1 350 420 455 -35 proj 2 200 250 260 -10 proj 3 500 700 650 50 We don’t like negative error Mean absolute error (MAE) = ∑| predicted cost – actual cost | / no of samples What is the mean absolute error of the model? 31.67 absolute
 Let’s try other Ws: Exercise 4: Construction Cost Model Estimated Construction Cost Actual Construction Cost y=1.3x y=1.4x y=1.5x proj 1 350 420 455 588 682.5 proj 2 200 250 260 350 390 proj 3 500 700 650 980 975 31.67 182.67 47.5 MAE Input (X) Output (Y) * W=1.3 Our Neural Net model:
 Let’s see if we can do better: Exercise 4: Construction Cost Model X Y * W + b Y = W*x + b input output weight bias
Exercise 4: Construction Cost Model W/b -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 1 177 167 157 147 137 127 117 107 97 87 77 67 57 53 50 1.1 142 132 122 112 102 92 82 72 62 52 42 42 45 48 52 1.2 107 97 87 77 67 57 47 37 33 37 40 43 47 50 53 1.3 72 62 52 42 35 32 28 32 35 38 42 45 48 58 68 1.4 37 33 30 27 23 27 30 33 43 53 63 73 83 93 103 1.5 25 22 18 28 38 48 58 68 78 88 98 108 118 128 138 1.6 33 43 53 63 73 83 93 103 113 123 133 143 153 163 173 1.7 68 78 88 98 108 118 128 138 148 158 168 178 188 198 208 1.8 103 113 123 133 143 153 163 173 183 193 203 213 223 233 243 1.9 138 148 158 168 178 188 198 208 218 228 238 248 258 268 278 2 173 183 193 203 213 223 233 243 253 263 273 283 293 303 313 ? Y=2x-70 Y=x Y=x+70 Y=2x+70 Y=x-70 What is the mean absolute error of these models?
Exercise 4: Construction Cost Model W/b -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 1 177 167 157 147 137 127 117 107 97 87 77 67 57 53 50 1.1 142 132 122 112 102 92 82 72 62 52 42 42 45 48 52 1.2 107 97 87 77 67 57 47 37 33 37 40 43 47 50 53 1.3 72 62 52 42 35 32 28 32 35 38 42 45 48 58 68 1.4 37 33 30 27 23 27 30 33 43 53 63 73 83 93 103 1.5 25 22 18 28 38 48 58 68 78 88 98 108 118 128 138 1.6 33 43 53 63 73 83 93 103 113 123 133 143 153 163 173 1.7 68 78 88 98 108 118 128 138 148 158 168 178 188 198 208 1.8 103 113 123 133 143 153 163 173 183 193 203 213 223 233 243 1.9 138 148 158 168 178 188 198 208 218 228 238 248 258 268 278 2 173 183 193 203 213 223 233 243 253 263 273 283 293 303 313 What model is better?
W/b -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 1 177 167 157 147 137 127 117 107 97 87 77 67 57 53 50 1.1 142 132 122 112 102 92 82 72 62 52 42 42 45 48 52 1.2 107 97 87 77 67 57 47 37 33 37 40 43 47 50 53 1.3 72 62 52 42 35 32 28 32 35 38 42 45 48 58 68 1.4 37 33 30 27 23 27 30 33 43 53 63 73 83 93 103 1.5 25 22 18 28 38 48 58 68 78 88 98 108 118 128 138 1.6 33 43 53 63 73 83 93 103 113 123 133 143 153 163 173 1.7 68 78 88 98 108 118 128 138 148 158 168 178 188 198 208 1.8 103 113 123 133 143 153 163 173 183 193 203 213 223 233 243 1.9 138 148 158 168 178 188 198 208 218 228 238 248 258 268 278 2 173 183 193 203 213 223 233 243 253 263 273 283 293 303 313 Exercise 4: Construction Cost Model X Y * W + b Y = 1.5*x -50 input output weight bias Congratulation! You have trained your first neural network model!!!
Exercise 4: Construction Cost Model Y = 1.5*x -50 Estimated Construction Cost Actual Construction Cost proj 1 350 420 proj 2 200 250 proj 3 500 700 Proj 4 400 ? Y = 1.5*400 -50 = 550 answer
ML Algorithms: Artificial Neural Network X1 Y *W1 + b X2 *W2 Y = W1x1+ W2x2 + b Estimated Construction Cost Average temperature Actual Construction Cost proj 1 350 62 420 proj 2 200 55 250 proj 3 500 85 700 Proj 4 400 53 ?
ML Algorithms: Artificial Neural Network X1 f *W1 + b X2 *W2 f = W1x1+ W2x2 + b T(f) Weighted sum of input Activation function Ŷ Predicted value
Exercise 5: X1 f *W1 + b X2 *W2 f = W1x1+ W2x2 + b T(f) Ŷ ? What is the predicted value (Ŷ) if: X1=0.2 , X2= -0.3 W1=0.5, W2=0.5 b= 0.1
Exercise 5: X1 f *W1 + b X2 *W2 f = W1x1+ W2x2 + b = 0.2*0.5-0.3*0.5+0.1=0.05 T(f)=T(0.05) = 1 T(f) Ŷ ? What is the predicted value if: X1=0.2 , X2= -0.3 W1=0.5, W2=0.5 b= 0.1
Some activation functions threshold sigmoid tanh ReLU
ML Algorithms: Artificial Neural Network Now, let’s talk about a group of neurons working together. X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ Hmmm… looks very messy. Let’s come up with a shorter from of a neuron.
ML Algorithms: Shorter form of a neuron X1 T(F=∑(XiWi+b)) *W1 b X2 *W2 X1 f *W1 + b X2 *W2 T(f) Ŷ
ML Algorithms: Short form of a neuron n X1 T(F=∑(XiWi+b)) *W1 b X2 *W2 layer Neuron at this layer
n n n n n n
Can you identify input/output/hidden layer?
Input layer Output layer Hidden layer
What is the error?
Error = |y- |
Demo in Weka
 What are the repetitive tasks that you do in your everyday job and ML can help you to automate? Exercise 5
 What are the problems that you have no idea how to solve or things that you have no idea how to do, and ML can help you to do or resolve it? Exercise 5
 Imaging you have a personal assistant agent that is like a trusted collaborator: it can brainstorm with you, it can do research for you, it can tell if your idea doesn’t work because of this and that, it can fill in the role of other parties (if you are an architect, it can provide you with the knowledge of engineering or construction). What are the things that you want your assistant to do for you. Exercise 5
Wrap up  What is AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms
Wrap up  What is AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms
Wrap up  What is AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms
Wrap up  What is AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms Model Outlook Temperature Humidity Windy Construction Input Output or label F(Outlook, Temperature, Humidity, Windy) = Ŷ
Wrap up  What is AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms
Wrap up  What is AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms
Wrap up  What is AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms  Decision Tree  Neural Network
Wrap up  What is AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms  Decision Tree  Neural Network
Autodesk and the Autodesk logo are registered trademarks or trademarks of Autodesk, Inc., and/or its subsidiaries and/or affiliates in the USA and/or other countries. All other brand names, product names, or trademarks belong to their respective holders. Autodesk reserves the right to alter product and services offerings, and specifications and pricing at any time without notice, and is not responsible for typographical or graphical errors that may appear in this document. © 2017 Autodesk. All rights reserved.

More Related Content

PPTX
Training and Testing Neural Network unit II
bytintu47
 
PDF
Camp IT: Making the World More Efficient Using AI & Machine Learning
byKrzysztof Kowalczyk
 
PPTX
Machine Learning Essentials Demystified part2 | Big Data Demystified
byOmid Vahdaty
 
PDF
Machine Learning and Deep Learning 4 dummies
byDori Waldman
 
PPTX
Deep learning from scratch
byEran Shlomo
 
PDF
Introduction to Machine Learning with Spark
bydatamantra
 
PDF
Introduction to Machine Learning
byBig_Data_Ukraine
 
PPTX
Machine Learning Essentials Demystified part1 | Big Data Demystified
byOmid Vahdaty
 
Training and Testing Neural Network unit II
bytintu47
 
Camp IT: Making the World More Efficient Using AI & Machine Learning
byKrzysztof Kowalczyk
 
Machine Learning Essentials Demystified part2 | Big Data Demystified
byOmid Vahdaty
 
Machine Learning and Deep Learning 4 dummies
byDori Waldman
 
Deep learning from scratch
byEran Shlomo
 
Introduction to Machine Learning with Spark
bydatamantra
 
Introduction to Machine Learning
byBig_Data_Ukraine
 
Machine Learning Essentials Demystified part1 | Big Data Demystified
byOmid Vahdaty
 

Similar to REvit training

PDF
Introduction to Machine Learning: Foundations and Applications
byremoved_2838c0f85dcbdf1a3b55b256d9455357
 
PDF
Artificial Neural Network for machine learning
by2303oyxxxjdeepak
 
PDF
Week_2_Neural_Networks_Basichhhhhhhs.pdf
byAliker5
 
PDF
Mlcc #4
byChung-Hsiang Ofa Hsueh
 
PDF
AI/ML Fundamentals to advanced Slides by GDG Amrita Mysuru.pdf
byLakshay14663
 
PDF
Reading Materials for Operational Research
byDerbew Tesfa
 
PDF
[update] Introductory Parts of the Book "Dive into Deep Learning"
byYoung-Min kang
 
PDF
Intro to Machine Learning with TF- workshop
byProttay Karim
 
PDF
Machine learning4dummies
byMichael Winer
 
PDF
Regression
byNcib Lotfi
 
PDF
From Software Engineering To Machine Learning
byAlexey Grigorev
 
PDF
Bootstrapping Machine Learning
byLouis Dorard
 
PPTX
Introduction to neural networks
byAhmad Hammoudeh
 
PDF
Capstone paper
byMuhammad Saeed
 
ODP
Machine learning 2016: deep networks and Monte Carlo Tree Search
byOlivier Teytaud
 
ODP
Machine learning 2016: deep networks and Monte Carlo Tree Search
byOlivier Teytaud
 
PDF
Machine Learning: Make Your Ruby Code Smarter
byAstrails
 
PDF
Demystifying Machine Learning
byVaideeswaran Sethuraman
 
PPTX
Nimrita deep learning
byNimrita Koul
 
PPTX
Machine learning
byVikas Sinha
 
Introduction to Machine Learning: Foundations and Applications
byremoved_2838c0f85dcbdf1a3b55b256d9455357
 
Artificial Neural Network for machine learning
by2303oyxxxjdeepak
 
Week_2_Neural_Networks_Basichhhhhhhs.pdf
byAliker5
 
Mlcc #4
byChung-Hsiang Ofa Hsueh
 
AI/ML Fundamentals to advanced Slides by GDG Amrita Mysuru.pdf
byLakshay14663
 
Reading Materials for Operational Research
byDerbew Tesfa
 
[update] Introductory Parts of the Book "Dive into Deep Learning"
byYoung-Min kang
 
Intro to Machine Learning with TF- workshop
byProttay Karim
 
Machine learning4dummies
byMichael Winer
 
Regression
byNcib Lotfi
 
From Software Engineering To Machine Learning
byAlexey Grigorev
 
Bootstrapping Machine Learning
byLouis Dorard
 
Introduction to neural networks
byAhmad Hammoudeh
 
Capstone paper
byMuhammad Saeed
 
Machine learning 2016: deep networks and Monte Carlo Tree Search
byOlivier Teytaud
 
Machine learning 2016: deep networks and Monte Carlo Tree Search
byOlivier Teytaud
 
Machine Learning: Make Your Ruby Code Smarter
byAstrails
 
Demystifying Machine Learning
byVaideeswaran Sethuraman
 
Nimrita deep learning
byNimrita Koul
 
Machine learning
byVikas Sinha
 

Recently uploaded

PPTX
CFP_Unit 5. Structure and Functions pptx
bypawarbhaktiit
 
PDF
EPC vs Reality: The Schedule That Was Never Possible
byGanesh Kumar
 
PDF
Java Programming core java and basics concepts
byHarish Khodke
 
PPTX
Design and Development of Friction Stir Welding joints of Aluminium and Coppe...
byNareshKumarSingh12
 
PPTX
Symmetrical faults in power system .pptx
byRased Khan
 
PPTX
Calculation of Water Hardness and softening.pptx
byChemical Engineering Dept. NIT Rourkela-769008, Odisha, India
 
PDF
Deterministic Finite Automata to Regular Expression Conversion.pdf
byNileshPardeshi28
 
PDF
BOE 2.1 Inch LCD 1600X1600 For VR AR Headset Smart Device
bySyluxdisplay
 
PDF
Non-Deterministic Finite Automata (NFA) to Deterministic Finite Automata (DFA...
byNileshPardeshi28
 
PPTX
CFP_Unit 3 Decision Control and Looping Statements
bypawarbhaktiit
 
PDF
graph graph graph theory Graph presentation .pdf
bygeekcooder2020
 
PDF
Stiffness Matrix Method of Analysis for beams and frames
byDr. Nitin Naik
 
PDF
UNIT02_TRE_GEOMETRIC DESIGN.pdf_________
byAaquib Ansari
 
PPTX
review of transistor circuit and applications
bysophieshuqinghuang
 
PPTX
analog communication part one lecture note
byhirutfanta1212
 
PPTX
Solar PV An Essential Requirement in renewable energy based sources Industria...
byshilpashukla2025
 
PPTX
Why Hydraulic Flushing Should Never Be Skipped Before Commissioning
byNeometrix_Engineering_Pvt_Ltd
 
PPTX
Steam Turbines basicsproperties power plant-1.pptx
byAjitParwani
 
PPTX
The Most Dangerous Asset in Your Plant Is the One You Ignore | CXO Guide to H...
byMaintWiz Technologies Private Limited
 
PPTX
TMD Maintenance Performance-compressed.pptx
bySVV Surya Narayana Raju
 
CFP_Unit 5. Structure and Functions pptx
bypawarbhaktiit
 
EPC vs Reality: The Schedule That Was Never Possible
byGanesh Kumar
 
Java Programming core java and basics concepts
byHarish Khodke
 
Design and Development of Friction Stir Welding joints of Aluminium and Coppe...
byNareshKumarSingh12
 
Symmetrical faults in power system .pptx
byRased Khan
 
Calculation of Water Hardness and softening.pptx
byChemical Engineering Dept. NIT Rourkela-769008, Odisha, India
 
Deterministic Finite Automata to Regular Expression Conversion.pdf
byNileshPardeshi28
 
BOE 2.1 Inch LCD 1600X1600 For VR AR Headset Smart Device
bySyluxdisplay
 
Non-Deterministic Finite Automata (NFA) to Deterministic Finite Automata (DFA...
byNileshPardeshi28
 
CFP_Unit 3 Decision Control and Looping Statements
bypawarbhaktiit
 
graph graph graph theory Graph presentation .pdf
bygeekcooder2020
 
Stiffness Matrix Method of Analysis for beams and frames
byDr. Nitin Naik
 
UNIT02_TRE_GEOMETRIC DESIGN.pdf_________
byAaquib Ansari
 
review of transistor circuit and applications
bysophieshuqinghuang
 
analog communication part one lecture note
byhirutfanta1212
 
Solar PV An Essential Requirement in renewable energy based sources Industria...
byshilpashukla2025
 
Why Hydraulic Flushing Should Never Be Skipped Before Commissioning
byNeometrix_Engineering_Pvt_Ltd
 
Steam Turbines basicsproperties power plant-1.pptx
byAjitParwani
 
The Most Dangerous Asset in Your Plant Is the One You Ignore | CXO Guide to H...
byMaintWiz Technologies Private Limited
 
TMD Maintenance Performance-compressed.pptx
bySVV Surya Narayana Raju
 

REvit training

  • 1.
    Join the conversation#AU2017 Join the conversation #AU2017 Introduction to Machine Learning for Building Design and Construction Mehdi Nourbakhsh, Ph.D. Sr. Research Scientist, Autodesk Research mehdi.nourbakhsh [at] Autodesk.com
  • 2.
     What isAI/ML?  History of ML  Application of ML in AEC  ML Glossary  ML Types  ML Process  ML Algorithms  Decision Tree  Artificial Neural Network  Wrap up Contents:
  • 3.
  • 4.
    History of AI/ML Alot more here
  • 5.
  • 6.
  • 7.
     Data, sample Variables, attributes, features  Feature vector  Input, output ML Glossary outlook temperature humidity windy construction Day 1 overcast hot high FALSE yes Day 2 rainy cool normal TRUE yes Day 3 sunny mild high FALSE no
  • 8.
    ML Glossary Feature Vector=(overcast, hot, high, FALSE)
  • 9.
  • 10.
    ML Glossary Model Outlook Temperature Humidity Windy Construction Input Outputor label F(Outlook, Temperature, Humidity, Windy) = Ŷ
  • 11.
    A1 A2 A3Y 1 0.2 Yes 0.32 5 0.56 No 0.58 2 0.56 Yes 0.23 3 0.6 Yes 0.39 Exercise 1 In the following example, what is/are: • Samples • Features • Input • output
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
    A1 A2 A3Y 1 0.2 Yes 0.32 5 0.56 No 0.58 2 0.56 Yes 0.23 3 0.6 Yes 0.39 Exercise 1.5 Is this a supervised or unsupervised learning? Is this a classification or regression problem?
  • 17.
     Donten ina company that has more than 10,000 building models in their database. These models are either residential, office space, or warehouses. They want to categorize their models into these three groups and seek your recommendation as a thought-leader in the industry. Which one do you recommend?  Hiring 20 people to manually open all of the models and categorize them in 10 months?  Using machine learning to automatically categorize the models in a month? (why does it take a month?) Exercise 2
  • 18.
     Which typeof ML is this?  How should we describe this problem to the machine?  Data?  Features?  Input, output? Exercise 2 Model ? ? … ? ? Input Output Residential Warehouse Office
  • 19.
    Simplified ML process Inpractice, this is not a straightforward process, there are lots of back and forth between the steps.
  • 20.
    ML Algorithms: DecisionTree Predict if construction will happen?
  • 21.
    Exercise 3: DecisionTree Take a guess…
  • 22.
    Exercise 3: DecisionTree Follow this algorithm: • Divide into subsets • Are they pure? (all ‘yes’ or ‘no’) • Yes->done • No-> repeat
  • 23.
    Exercise 3: DecisionTree 9 Yes / 4 No Follow this algorithm : • Divide into subsets • Are they pure? (all ‘yes’ or ‘no’) • Yes->done • No-> repeat
  • 24.
    Exercise 3: DecisionTree 9 Yes / 4 No Follow this algorithm : • Divide into subsets • Are they pure? (all ‘yes’ or ‘no’) • Yes->done • No-> repeat
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
    ML Algorithms: ArtificialNeural Network Source: https://www.intechopen.com/source/html/39067/media/image1.png A) human neuron; B) artificial neuron; C) biological synapse; D) ANN synapses
  • 33.
     A constructioncompany completed three projects. The estimated and actual construction cost of each project is presented in the following table. Your task is to design a cost model for the company based on the available data and predict how much is the actual cost of ‘proj 4’. Exercise 4: Construction Cost Model Estimated Construction Cost Actual Construction Cost proj 1 350 420 proj 2 200 250 proj 3 500 700 Proj 4 400 ?
  • 34.
     A constructioncompany completed three projects. The estimated and actual construction cost of each project is presented in the following table. Your task is to design a cost model for the company based on the available data. Exercise 4: Construction Cost Model Input (X) Output (Y) * W Estimated Construction Cost Actual Construction Cost proj 1 350 420 proj 2 200 250 proj 3 500 700 Proj 4 400 ?
  • 35.
     One wayof doing it:  A= Sum all estimated costs  B = Sum all actual costs  W = B/A Exercise 4: Construction Cost Model Estimated Construction Cost Actual Construction Cost proj 1 350 420 proj 2 200 250 proj 3 500 700 Sum 1050 1370 B/A 1.30 Is it good? Why not 1.5 or 2.0? Y=1.30X
  • 36.
     Measure ofgoodness = less error  What is the error of the model? Exercise 4: Construction Cost Model Estimated Construction Cost Actual Construction Cost Predicted Cost (y=1.3x) Error proj 1 350 420 455 ? proj 2 200 250 260 ? proj 3 500 700 650 ?
  • 37.
    Exercise 4: ConstructionCost Model Absolute error = | predicted cost – actual cost | Estimated Construction Cost Actual Construction Cost Predicted Cost (y=1.3x) Error proj 1 350 420 455 -35 proj 2 200 250 260 -10 proj 3 500 700 650 50 We don’t like negative error Mean absolute error (MAE) = ∑| predicted cost – actual cost | / no of samples What is the mean absolute error of the model? 31.67 absolute
  • 38.
     Let’s tryother Ws: Exercise 4: Construction Cost Model Estimated Construction Cost Actual Construction Cost y=1.3x y=1.4x y=1.5x proj 1 350 420 455 588 682.5 proj 2 200 250 260 350 390 proj 3 500 700 650 980 975 31.67 182.67 47.5 MAE Input (X) Output (Y) * W=1.3 Our Neural Net model:
  • 39.
     Let’s seeif we can do better: Exercise 4: Construction Cost Model X Y * W + b Y = W*x + b input output weight bias
  • 40.
    Exercise 4: ConstructionCost Model W/b -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 1 177 167 157 147 137 127 117 107 97 87 77 67 57 53 50 1.1 142 132 122 112 102 92 82 72 62 52 42 42 45 48 52 1.2 107 97 87 77 67 57 47 37 33 37 40 43 47 50 53 1.3 72 62 52 42 35 32 28 32 35 38 42 45 48 58 68 1.4 37 33 30 27 23 27 30 33 43 53 63 73 83 93 103 1.5 25 22 18 28 38 48 58 68 78 88 98 108 118 128 138 1.6 33 43 53 63 73 83 93 103 113 123 133 143 153 163 173 1.7 68 78 88 98 108 118 128 138 148 158 168 178 188 198 208 1.8 103 113 123 133 143 153 163 173 183 193 203 213 223 233 243 1.9 138 148 158 168 178 188 198 208 218 228 238 248 258 268 278 2 173 183 193 203 213 223 233 243 253 263 273 283 293 303 313 ? Y=2x-70 Y=x Y=x+70 Y=2x+70 Y=x-70 What is the mean absolute error of these models?
  • 41.
    Exercise 4: ConstructionCost Model W/b -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 1 177 167 157 147 137 127 117 107 97 87 77 67 57 53 50 1.1 142 132 122 112 102 92 82 72 62 52 42 42 45 48 52 1.2 107 97 87 77 67 57 47 37 33 37 40 43 47 50 53 1.3 72 62 52 42 35 32 28 32 35 38 42 45 48 58 68 1.4 37 33 30 27 23 27 30 33 43 53 63 73 83 93 103 1.5 25 22 18 28 38 48 58 68 78 88 98 108 118 128 138 1.6 33 43 53 63 73 83 93 103 113 123 133 143 153 163 173 1.7 68 78 88 98 108 118 128 138 148 158 168 178 188 198 208 1.8 103 113 123 133 143 153 163 173 183 193 203 213 223 233 243 1.9 138 148 158 168 178 188 198 208 218 228 238 248 258 268 278 2 173 183 193 203 213 223 233 243 253 263 273 283 293 303 313 What model is better?
  • 42.
    W/b -70 -60-50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 1 177 167 157 147 137 127 117 107 97 87 77 67 57 53 50 1.1 142 132 122 112 102 92 82 72 62 52 42 42 45 48 52 1.2 107 97 87 77 67 57 47 37 33 37 40 43 47 50 53 1.3 72 62 52 42 35 32 28 32 35 38 42 45 48 58 68 1.4 37 33 30 27 23 27 30 33 43 53 63 73 83 93 103 1.5 25 22 18 28 38 48 58 68 78 88 98 108 118 128 138 1.6 33 43 53 63 73 83 93 103 113 123 133 143 153 163 173 1.7 68 78 88 98 108 118 128 138 148 158 168 178 188 198 208 1.8 103 113 123 133 143 153 163 173 183 193 203 213 223 233 243 1.9 138 148 158 168 178 188 198 208 218 228 238 248 258 268 278 2 173 183 193 203 213 223 233 243 253 263 273 283 293 303 313 Exercise 4: Construction Cost Model X Y * W + b Y = 1.5*x -50 input output weight bias Congratulation! You have trained your first neural network model!!!
  • 43.
    Exercise 4: ConstructionCost Model Y = 1.5*x -50 Estimated Construction Cost Actual Construction Cost proj 1 350 420 proj 2 200 250 proj 3 500 700 Proj 4 400 ? Y = 1.5*400 -50 = 550 answer
  • 44.
    ML Algorithms: ArtificialNeural Network X1 Y *W1 + b X2 *W2 Y = W1x1+ W2x2 + b Estimated Construction Cost Average temperature Actual Construction Cost proj 1 350 62 420 proj 2 200 55 250 proj 3 500 85 700 Proj 4 400 53 ?
  • 45.
    ML Algorithms: ArtificialNeural Network X1 f *W1 + b X2 *W2 f = W1x1+ W2x2 + b T(f) Weighted sum of input Activation function Ŷ Predicted value
  • 46.
    Exercise 5: X1 f *W1 + b X2*W2 f = W1x1+ W2x2 + b T(f) Ŷ ? What is the predicted value (Ŷ) if: X1=0.2 , X2= -0.3 W1=0.5, W2=0.5 b= 0.1
  • 47.
    Exercise 5: X1 f *W1 + b X2*W2 f = W1x1+ W2x2 + b = 0.2*0.5-0.3*0.5+0.1=0.05 T(f)=T(0.05) = 1 T(f) Ŷ ? What is the predicted value if: X1=0.2 , X2= -0.3 W1=0.5, W2=0.5 b= 0.1
  • 48.
  • 49.
    ML Algorithms: ArtificialNeural Network Now, let’s talk about a group of neurons working together. X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ X1 f *W 1 + b X2 *W 2 T(f) Ŷ Hmmm… looks very messy. Let’s come up with a shorter from of a neuron.
  • 50.
    ML Algorithms: Shorterform of a neuron X1 T(F=∑(XiWi+b)) *W1 b X2 *W2 X1 f *W1 + b X2 *W2 T(f) Ŷ
  • 51.
    ML Algorithms: Shortform of a neuron n X1 T(F=∑(XiWi+b)) *W1 b X2 *W2 layer Neuron at this layer
  • 52.
  • 53.
    Can you identifyinput/output/hidden layer?
  • 54.
    Input layer Outputlayer Hidden layer
  • 56.
  • 57.
  • 58.
  • 59.
     What arethe repetitive tasks that you do in your everyday job and ML can help you to automate? Exercise 5
  • 60.
     What arethe problems that you have no idea how to solve or things that you have no idea how to do, and ML can help you to do or resolve it? Exercise 5
  • 61.
     Imaging youhave a personal assistant agent that is like a trusted collaborator: it can brainstorm with you, it can do research for you, it can tell if your idea doesn’t work because of this and that, it can fill in the role of other parties (if you are an architect, it can provide you with the knowledge of engineering or construction). What are the things that you want your assistant to do for you. Exercise 5
  • 62.
    Wrap up  Whatis AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms
  • 63.
    Wrap up  Whatis AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms
  • 64.
    Wrap up  Whatis AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms
  • 65.
    Wrap up  Whatis AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms Model Outlook Temperature Humidity Windy Construction Input Output or label F(Outlook, Temperature, Humidity, Windy) = Ŷ
  • 66.
    Wrap up  Whatis AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms
  • 67.
    Wrap up  Whatis AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms
  • 68.
    Wrap up  Whatis AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms  Decision Tree  Neural Network
  • 69.
    Wrap up  Whatis AI/ML?  History of ML  Application of ML  ML Glossary  ML Types  ML Process  ML Algorithms  Decision Tree  Neural Network
  • 70.
    Autodesk and theAutodesk logo are registered trademarks or trademarks of Autodesk, Inc., and/or its subsidiaries and/or affiliates in the USA and/or other countries. All other brand names, product names, or trademarks belong to their respective holders. Autodesk reserves the right to alter product and services offerings, and specifications and pricing at any time without notice, and is not responsible for typographical or graphical errors that may appear in this document. © 2017 Autodesk. All rights reserved.