CS代考程序代写 algorithm case study chain deep learning AI WEEK 1 TERM 2:

WEEK 1 TERM 2:
MSIN0097
Predictive Analytics
A P MOORE

PREDICTIVE ANALYTICS
A bit about me

MY BACKGROUND CURRENT | PREVIOUS
Alastair Moore
Head of Analytics and Machine Learning
Senior Teaching Fellow Predictive Analytics
MSc Business Analytics
UCL School of Management
MBA Programme,
Innovation and Entrepreneurship in Europe Emerging Business Technologies
BiMBA Peking University
Co-Founder Satalia.com
Co-Founder WeArePopUp.com
Par tner AMAAMS LLP

SATALIA.COM

MISHCON DE REYA

OPEN BANKING
WORKING WITH NESTA ON CHALLENGE LEAD APPROACHES
Source: www.openupchallenge.io

DLT SYSTEMS
USE OF DISTRIBUTED LEDGERS IN REAL ESTATE
How will we transact property in the future?
Source: www.gov.uk/government/news/hm-land-registry-to-explore-the-benefits-of-blockchain

RESEARCH INTERESTS

PREDICTIVE ANALYTICS
Course overview

TODAY
LECTURE / WORKSHOP SCHEDULE
~90 mins
8:30 am
9:30 am
10:30 am
11:30 am
Video 1
Video 2
Video 3
Asynchronous: Video lectures
1hr
2hr
3hr
90 mins
Review
Problem 1
Discussion
Problem 2
Discussion
Synchronous: Lectures/Tutorials

HANDS ON MACHINE LEARNING GIT
Week 1 Week 2
Week 3
Week 6
Week 7 Week 8
Source: https://github.com/ageron/handson-ml
Week 4 Week 5

TEACHING SUPPORT
Kamil Tylinski Teaching Assistant
kamil.tylinski.16@ucl.ac.uk
Jiangbo Shangguan Teaching Assistant
j.shangguan.17@ucl.ac.uk
Bartos Kultys Teaching Assistant
bartosz.kultys.18@ucl.ac.uk
Editha Nemsic
Teaching Assistant
editha.nemsic.19@ucl.ac.uk
Dr Viviana Culmone Teaching Assistant
v.culmone@ucl.ac.uk
Walter Hernandez
Teaching Assistant
walter.hernandez.18@ucl.ac.uk

ASSESSMEN TS
— Individual Coursework – 60% – 2000words
– Due Date: Friday 26th February 2021
— Group Coursework – 40% – 4-5pergroup
– Deadline for Group formation: Friday 29th January 2021 (week 3) – 2000words
– Due Date:Thursday 18th March 2021

PREDICTIVE ANALYTICS
Review

DEEP LEARNING STATE OF THE ART
– Initial human generated text
– Part automated text generation
– Automated video generation
– Automated audio generation
– Automated translation

WAVES OF INVESTMENT THE ROLE OF INDUSTRIALIZATION
https://machinelearnings.co/winning-strategies-for-applied-ai-companies-f02cac0a6ad8

TOWER & MOAT
Source: https://blog.gardeviance.org/2014/07/tower-and-moat.html

MACHINE LEARNING JARGON
— Model
— Interpolating / Extrapolating — Data Bias
— Noise / Outliers
— Learning algorithm
— Inference algorithm
— Supervised learning
— Unsupervised learning
— Classification
— Regression
— Clustering
— Decomposition
— Parameters
— Optimisation
— Training data
— Testing data
— Error metric
— Linear model
— Parametric model
— Model variance
— Model bias
— Model generalization
— Overfitting
— Goodness-of-fit
— Hyper-parameters
— Failure modes
— Confusion matrix
— True Positive
— False Negative
— Data density
— Partition
— Hidden parameter
— High dimensional space
— Low dimensional space
— Separable data
— Manifold / Decision surface
— Hyper cube / volume / plane

机器学习 行话
— 模型
— 内插 / 外推 — 数据偏差
— 噪声/离群值 — 学习算法
— 推断算法
— 监督学习
— 无监督学习 — 分类
— 回归
— 聚类
— 分解
— 参数
— 优化
— 训练数据 — 测试数据 — 误差指标 — 线性模型 — 参数模型 — 模型方差 — 模型偏差 — 模型泛化 — 过拟合 — 拟合优度 — 超参数
— 失败模式
— 混淆矩阵
— 真正例
— 假反例
— 数据密度
— 划分
— 隐藏参数
— 高维空间
— 低维空间
— 可分数据
— 流形/ 决策面
— 超立方体/超体积/超平 面

MODEL / 模型 MÓXÍNG

MACHINE LEARNING
Data + modelàprediction

MACHINE LEARNING DATA DRIVEN AI
Assume there is enough data to find statistical associations to solve specific tasks
Data + modelàprediction
Define how well the model solves the task and adapt the parameters to maximize performance

LEARNING A FUNCTION
𝑥→𝑦
𝑥 →𝑓(𝑥)→𝑦

LEARNING A FUNCTION
𝑥→𝑦
𝑥 →𝑓(𝑥)→𝑦
Measured data
Features Inferred/Predicted/Estimated value
Trueinitialvalue𝑥 →𝑥’→𝑓 𝑥 =𝑦’ →𝑦
(world state) True target value
Learned/Fitted function (world state) From n observations

LEARNING A FUNCTION
𝑥→𝑦
𝑥 →𝑓(𝑥)→𝑦
Measured data
Features Inferred/Predicted/Estimated value
Trueinitialvalue𝑥 →𝑥’→𝑓 𝑥 =𝑦’ →𝑦
(world state) True target value
Learned/Fitted function (world state) From n observations
input 𝑥→ 𝑓 𝑥 →𝑦 output

INTERPOLATING / 内插 NÈI CHĀ

EXTRAPOLATING / 外推 WAÌ TUĪ

NOISE, OUTLIERS / 噪声，离群值 ZÀOSHĒNG , LÍ QÚN ZHÍ

LEARNING ALGORITHM / 学习算法 XUÉXÍ SUÀNFǍ

INFERENCE ALGORITHM / 推断算法 TUĪDUÀN SUÀNFǍ

SUPERVISED LEARNING / 监督学习 JIĀNDŪ XUÉXÍ

UNSUPERVISED LEARNING / 无监督学习 WÚ JIĀNDŪ XUÉXÍ

CLASSIFICATION / 分类 FĒNLÈI

REGRESSION / 回归分析 HUÍGUĪ FĒNXĪ

CLUSTERING / 聚类 JÙ LÈI

DECOMPOSITION / 分解 FĒNJIĚ

PREDICTIVE ANALYTICS
Perception, Patterns and Gestalt

PREDICTIVE ANALYTICS
The increase in computing power

TRAINING TIMES

PREDICTIVE ANALYTICS
Problem 1
~15 mins group work ~15 mins discussion

PRACTICAL TOOLS ML CANVAS

The Machine Lea􏰈ning Can􏰉a􏰊 (􏰉0.4)​ ​De􏰊ig􏰋ed f􏰌􏰈: ​ ​ De􏰊ig􏰋ed b􏰍: ​ ​ Da􏰎e: ​ ​ I􏰎e􏰈a􏰎i􏰌􏰋: ​ .
Learn
Deci􏰊i􏰌n􏰊
H􏰌􏰏 a􏰈e 􏰐􏰈edic􏰎i􏰌􏰋􏰊 􏰑􏰊ed 􏰎􏰌
􏰒ake deci􏰊i􏰌􏰋􏰊 􏰎ha􏰎 􏰐􏰈􏰌􏰉ide
􏰎he 􏰐􏰈􏰌􏰐􏰌􏰊ed 􏰉al􏰑e 􏰎􏰌 􏰎he e􏰋d-􏰑􏰊e􏰈?
ML 􏰎a􏰊k
I􏰋􏰐􏰑􏰎, 􏰌􏰑􏰎􏰐􏰑􏰎 􏰎􏰌 􏰐􏰈edic􏰎, 􏰎􏰍􏰐e 􏰌f 􏰐􏰈􏰌ble􏰒.
Val􏰑e P􏰈􏰌􏰐􏰌􏰊i􏰎i􏰌n􏰊
Wha􏰎 a􏰈e 􏰏e 􏰎􏰈􏰍i􏰋g 􏰎􏰌 d􏰌 f􏰌􏰈 􏰎he e􏰋d-􏰑􏰊e􏰈(􏰊) 􏰌f 􏰎he 􏰐􏰈edic􏰎i􏰉e 􏰊􏰍􏰊􏰎e􏰒? Wha􏰎 􏰌bjec􏰎i􏰉e􏰊 a􏰈e 􏰏e 􏰊e􏰈􏰉i􏰋g?
Da􏰎a S􏰌􏰑􏰈ce􏰊
Which 􏰈a􏰏 da􏰎a 􏰊􏰌􏰑􏰈ce􏰊 ca􏰋 􏰏e 􏰑􏰊e (i􏰋􏰎e􏰈􏰋al a􏰋d e􏰓􏰎e􏰈􏰋al)?
C􏰌llec􏰎ing Da􏰎a
H􏰌􏰏 d􏰌 􏰏e ge􏰎 􏰋e􏰏 da􏰎a 􏰎􏰌 lea􏰈􏰋 f􏰈􏰌􏰒 (i􏰋􏰐􏰑􏰎􏰊 a􏰋d 􏰌􏰑􏰎􏰐􏰑􏰎􏰊)?
Fea􏰎􏰑􏰈e􏰊
I􏰋􏰐􏰑􏰎 􏰈e􏰐􏰈e􏰊e􏰋􏰎a􏰎i􏰌􏰋􏰊 e􏰓􏰎􏰈ac􏰎ed f􏰈􏰌􏰒 􏰈a􏰏 da􏰎a 􏰊􏰌􏰑􏰈ce􏰊.
B􏰑ilding M􏰌del􏰊
Whe􏰋 d􏰌 􏰏e c􏰈ea􏰎e/􏰑􏰐da􏰎e
􏰒􏰌del􏰊 􏰏i􏰎h 􏰋e􏰏 􏰎􏰈ai􏰋i􏰋g
da􏰎a? H􏰌􏰏 l􏰌􏰋g d􏰌 􏰏e ha􏰉e 􏰎􏰌 fea􏰎􏰑􏰈i􏰔e 􏰎􏰈ai􏰋i􏰋g i􏰋􏰐􏰑􏰎􏰊 a􏰋d c􏰈ea􏰎e a 􏰒􏰌del?
Making P􏰈edic􏰎i􏰌n􏰊
Whe􏰋 d􏰌 􏰏e 􏰒ake 􏰐􏰈edic􏰎i􏰌􏰋􏰊 􏰌􏰋 􏰋e􏰏 i􏰋􏰐􏰑􏰎􏰊? H􏰌􏰏 l􏰌􏰋g d􏰌 􏰏e ha􏰉e 􏰎􏰌 fea􏰎􏰑􏰈i􏰔e a 􏰋e􏰏 i􏰋􏰐􏰑􏰎 a􏰋d 􏰒ake a 􏰐􏰈edic􏰎i􏰌􏰋?
Offline E􏰉al􏰑a􏰎i􏰌n
Me􏰎h􏰌d􏰊 a􏰋d 􏰒e􏰎􏰈ic􏰊 􏰎􏰌 e􏰉al􏰑a􏰎e 􏰎he 􏰊􏰍􏰊􏰎e􏰒 bef􏰌􏰈e de􏰐l􏰌􏰍􏰒e􏰋􏰎.
Li􏰉e E􏰉al􏰑a􏰎i􏰌n and M􏰌ni􏰎􏰌􏰈ing
Me􏰎h􏰌d􏰊 a􏰋d 􏰒e􏰎􏰈ic􏰊 􏰎􏰌 e􏰉al􏰑a􏰎e 􏰎he 􏰊􏰍􏰊􏰎e􏰒 af􏰎e􏰈 de􏰐l􏰌􏰍􏰒e􏰋􏰎, a􏰋d 􏰎􏰌 􏰕􏰑a􏰋􏰎if􏰍 􏰉al􏰑e c􏰈ea􏰎i􏰌􏰋.
machinelea􏰈ningcan􏰉a􏰊.c􏰌m​ b􏰍 L􏰌􏰑i􏰊 D􏰌􏰈a􏰈d, Ph.D. ​Lice􏰋􏰊ed 􏰑􏰋de􏰈 a C􏰈ea􏰎i􏰉e C􏰌􏰒􏰒􏰌􏰋􏰊 A􏰎􏰎􏰈ib􏰑􏰎i􏰌􏰋-Sha􏰈eAlike 4.0 I􏰋􏰎e􏰈􏰋a􏰎i􏰌􏰋al Lice􏰋􏰊e.

The Machine Lea􏰈ning Can􏰉a􏰊 (􏰉0.4)​ ​De􏰊ig􏰋ed f􏰌􏰈: ​ ​ De􏰊ig􏰋ed b􏰍: ​ ​ Da􏰎e: ​ ​ I􏰎e􏰈a􏰎i􏰌􏰋: ​ .
Deci􏰊i􏰌n􏰊
H􏰌􏰏 a􏰈e 􏰐􏰈edic􏰎i􏰌􏰋􏰊 􏰑􏰊ed 􏰎􏰌
􏰒ake deci􏰊i􏰌􏰋􏰊 􏰎ha􏰎 􏰐􏰈􏰌􏰉ide
􏰎he 􏰐􏰈􏰌􏰐􏰌􏰊ed 􏰉al􏰑e 􏰎􏰌 􏰎he e􏰋d-􏰑􏰊e􏰈?
ML 􏰎a􏰊k
I􏰋􏰐􏰑􏰎, 􏰌􏰑􏰎􏰐􏰑􏰎 􏰎􏰌 􏰐􏰈edic􏰎, 􏰎􏰍􏰐e 􏰌f 􏰐􏰈􏰌ble􏰒.
Making P􏰈edic􏰎i􏰌n􏰊
Whe􏰋 d􏰌 􏰏e 􏰒ake 􏰐􏰈edic􏰎i􏰌􏰋􏰊 􏰌􏰋 􏰋e􏰏 i􏰋􏰐􏰑􏰎􏰊? H􏰌􏰏 l􏰌􏰋g d􏰌 􏰏e ha􏰉e 􏰎􏰌 fea􏰎􏰑􏰈i􏰔e a 􏰋e􏰏 i􏰋􏰐􏰑􏰎 a􏰋d 􏰒ake a 􏰐􏰈edic􏰎i􏰌􏰋?
Offline E􏰉al􏰑a􏰎i􏰌n
Me􏰎h􏰌d􏰊 a􏰋d 􏰒e􏰎􏰈ic􏰊 􏰎􏰌 e􏰉al􏰑a􏰎e 􏰎he 􏰊􏰍􏰊􏰎e􏰒 bef􏰌􏰈e de􏰐l􏰌􏰍􏰒e􏰋􏰎.
Val􏰑e P􏰈􏰌􏰐􏰌􏰊i􏰎i􏰌n􏰊
Wha􏰎 a􏰈e 􏰏e 􏰎􏰈􏰍i􏰋g 􏰎􏰌 d􏰌 f􏰌􏰈 􏰎he e􏰋d-􏰑􏰊e􏰈(􏰊) 􏰌f 􏰎he 􏰐􏰈edic􏰎i􏰉e 􏰊􏰍􏰊􏰎e􏰒? Wha􏰎 􏰌bjec􏰎i􏰉e􏰊 a􏰈e 􏰏e 􏰊e􏰈􏰉i􏰋g?
Da􏰎a S􏰌􏰑􏰈ce􏰊
Which 􏰈a􏰏 da􏰎a 􏰊􏰌􏰑􏰈ce􏰊 ca􏰋 􏰏e 􏰑􏰊e (i􏰋􏰎e􏰈􏰋al a􏰋d e􏰓􏰎e􏰈􏰋al)?
C􏰌llec􏰎ing Da􏰎a
H􏰌􏰏 d􏰌 􏰏e ge􏰎 􏰋e􏰏 da􏰎a 􏰎􏰌 lea􏰈􏰋 f􏰈􏰌􏰒 (i􏰋􏰐􏰑􏰎􏰊 a􏰋d 􏰌􏰑􏰎􏰐􏰑􏰎􏰊)?
Fea􏰎􏰑􏰈e􏰊
I􏰋􏰐􏰑􏰎 􏰈e􏰐􏰈e􏰊e􏰋􏰎a􏰎i􏰌􏰋􏰊 e􏰓􏰎􏰈ac􏰎ed f􏰈􏰌􏰒 􏰈a􏰏 da􏰎a 􏰊􏰌􏰑􏰈ce􏰊.
B􏰑ilding M􏰌del􏰊
Whe􏰋 d􏰌 􏰏e c􏰈ea􏰎e/􏰑􏰐da􏰎e
􏰒􏰌del􏰊 􏰏i􏰎h 􏰋e􏰏 􏰎􏰈ai􏰋i􏰋g
da􏰎a? H􏰌􏰏 l􏰌􏰋g d􏰌 􏰏e ha􏰉e 􏰎􏰌 fea􏰎􏰑􏰈i􏰔e 􏰎􏰈ai􏰋i􏰋g i􏰋􏰐􏰑􏰎􏰊 a􏰋d c􏰈ea􏰎e a 􏰒􏰌del?
Li􏰉e E􏰉al􏰑a􏰎i􏰌n and M􏰌ni􏰎􏰌􏰈ing
Me􏰎h􏰌d􏰊 a􏰋d 􏰒e􏰎􏰈ic􏰊 􏰎􏰌 e􏰉al􏰑a􏰎e 􏰎he 􏰊􏰍􏰊􏰎e􏰒 af􏰎e􏰈 de􏰐l􏰌􏰍􏰒e􏰋􏰎, a􏰋d 􏰎􏰌 􏰕􏰑a􏰋􏰎if􏰍 􏰉al􏰑e c􏰈ea􏰎i􏰌􏰋.
Learn
machinelea􏰈ningcan􏰉a􏰊.c􏰌m​ b􏰍 L􏰌􏰑i􏰊 D􏰌􏰈a􏰈d, Ph.D. ​Lice􏰋􏰊ed 􏰑􏰋de􏰈 a C􏰈ea􏰎i􏰉e C􏰌􏰒􏰒􏰌􏰋􏰊 A􏰎􏰎􏰈ib􏰑􏰎i􏰌􏰋-Sha􏰈eAlike 4.0 I􏰋􏰎e􏰈􏰋a􏰎i􏰌􏰋al Lice􏰋􏰊e.

The Machine Lea􏰈ning Can􏰉a􏰊 (􏰉0.4)​ ​De􏰊ig􏰋ed f􏰌􏰈: ​ ​ De􏰊ig􏰋ed b􏰍: ​ ​ Da􏰎e: ​ ​ I􏰎e􏰈a􏰎i􏰌􏰋: ​ . Domain
Deci􏰊i􏰌n􏰊
H􏰌􏰏 a􏰈e 􏰐􏰈edic􏰎i􏰌􏰋􏰊 􏰑􏰊ed 􏰎􏰌
􏰒ake deci􏰊i􏰌􏰋􏰊 􏰎ha􏰎 􏰐􏰈􏰌􏰉ide
􏰎he 􏰐􏰈􏰌􏰐􏰌􏰊ed 􏰉al􏰑e 􏰎􏰌 􏰎he e􏰋d-􏰑􏰊e􏰈?
ML 􏰎a􏰊k
I􏰋􏰐􏰑􏰎, 􏰌􏰑􏰎􏰐􏰑􏰎 􏰎􏰌 􏰐􏰈edic􏰎, 􏰎􏰍􏰐e 􏰌f 􏰐􏰈􏰌ble􏰒.
Val􏰑e P􏰈􏰌􏰐􏰌􏰊i􏰎i􏰌n􏰊
Wha􏰎 a􏰈e 􏰏e 􏰎􏰈􏰍i􏰋g 􏰎􏰌 d􏰌 f􏰌􏰈 􏰎he e􏰋d-􏰑􏰊e􏰈(􏰊) 􏰌f 􏰎he 􏰐􏰈edic􏰎i􏰉e 􏰊􏰍􏰊􏰎e􏰒? Wha􏰎 􏰌bjec􏰎i􏰉e􏰊 a􏰈e 􏰏e 􏰊e􏰈􏰉i􏰋g?
Da􏰎a S􏰌􏰑􏰈ce􏰊
Which 􏰈a􏰏 da􏰎a 􏰊􏰌􏰑􏰈ce􏰊 ca􏰋 􏰏e 􏰑􏰊e (i􏰋􏰎e􏰈􏰋al a􏰋d e􏰓􏰎e􏰈􏰋al)?
C􏰌llec􏰎ing Da􏰎a
H􏰌􏰏 d􏰌 􏰏e ge􏰎 􏰋e􏰏 da􏰎a 􏰎􏰌 lea􏰈􏰋 f􏰈􏰌􏰒 (i􏰋􏰐􏰑􏰎􏰊 a􏰋d 􏰌􏰑􏰎􏰐􏰑􏰎􏰊)?
Making P􏰈edic􏰎i􏰌n􏰊
Whe􏰋 d􏰌 􏰏e 􏰒ake 􏰐􏰈edic􏰎i􏰌􏰋􏰊 􏰌􏰋 􏰋e􏰏 i􏰋􏰐􏰑􏰎􏰊? H􏰌􏰏 l􏰌􏰋g d􏰌 􏰏e ha􏰉e 􏰎􏰌 fea􏰎􏰑􏰈i􏰔e a 􏰋e􏰏 i􏰋􏰐􏰑􏰎 a􏰋d 􏰒ake a 􏰐􏰈edic􏰎i􏰌􏰋?
Offline E􏰉al􏰑a􏰎i􏰌n
Me􏰎h􏰌d􏰊 a􏰋d 􏰒e􏰎􏰈ic􏰊 􏰎􏰌 e􏰉al􏰑a􏰎e 􏰎he 􏰊􏰍􏰊􏰎e􏰒 bef􏰌􏰈e de􏰐l􏰌􏰍􏰒e􏰋􏰎.
Fea􏰎􏰑􏰈e􏰊
I􏰋􏰐􏰑􏰎 􏰈e􏰐􏰈e􏰊e􏰋􏰎a􏰎i􏰌􏰋􏰊 e􏰓􏰎􏰈ac􏰎ed f􏰈􏰌􏰒 􏰈a􏰏 da􏰎a 􏰊􏰌􏰑􏰈ce􏰊.
B􏰑ilding M􏰌del􏰊
Whe􏰋 d􏰌 􏰏e c􏰈ea􏰎e/􏰑􏰐da􏰎e
􏰒􏰌del􏰊 􏰏i􏰎h 􏰋e􏰏 􏰎􏰈ai􏰋i􏰋g
da􏰎a? H􏰌􏰏 l􏰌􏰋g d􏰌 􏰏e ha􏰉e 􏰎􏰌 fea􏰎􏰑􏰈i􏰔e 􏰎􏰈ai􏰋i􏰋g i􏰋􏰐􏰑􏰎􏰊 a􏰋d c􏰈ea􏰎e a 􏰒􏰌del?
Li􏰉e E􏰉al􏰑a􏰎i􏰌n and M􏰌ni􏰎􏰌􏰈ing
Me􏰎h􏰌d􏰊 a􏰋d 􏰒e􏰎􏰈ic􏰊 􏰎􏰌 e􏰉al􏰑a􏰎e 􏰎he 􏰊􏰍􏰊􏰎e􏰒 af􏰎e􏰈 de􏰐l􏰌􏰍􏰒e􏰋􏰎, a􏰋d 􏰎􏰌 􏰕􏰑a􏰋􏰎if􏰍 􏰉al􏰑e c􏰈ea􏰎i􏰌􏰋.
machinelea􏰈ningcan􏰉a􏰊.c􏰌m​ b􏰍 L􏰌􏰑i􏰊 D􏰌􏰈a􏰈d, Ph.D. ​Lice􏰋􏰊ed 􏰑􏰋de􏰈 a C􏰈ea􏰎i􏰉e C􏰌􏰒􏰒􏰌􏰋􏰊 A􏰎􏰎􏰈ib􏰑􏰎i􏰌􏰋-Sha􏰈eAlike 4.0 I􏰋􏰎e􏰈􏰋a􏰎i􏰌􏰋al Lice􏰋􏰊e.

The Machine Lea􏰈ning Can􏰉a􏰊 (􏰉0.4)​ ​De􏰊ig􏰋ed f􏰌􏰈: ​ Deci􏰊i􏰌n􏰊
H􏰌􏰏 a􏰈e 􏰐􏰈edic􏰎i􏰌􏰋􏰊 􏰑􏰊ed 􏰎􏰌
􏰒ake deci􏰊i􏰌􏰋􏰊 􏰎ha􏰎 􏰐􏰈􏰌􏰉ide
􏰎he 􏰐􏰈􏰌􏰐􏰌􏰊ed 􏰉al􏰑e 􏰎􏰌 􏰎he e􏰋d-􏰑􏰊e􏰈?
​ De􏰊ig􏰋ed b􏰍: ​
​ Da􏰎e: ​
​ I􏰎e􏰈a􏰎i􏰌􏰋: ​ .
Prediction Engine
ML 􏰎a􏰊k
I􏰋􏰐􏰑􏰎, 􏰌􏰑􏰎􏰐􏰑􏰎 􏰎􏰌 􏰐􏰈edic􏰎, 􏰎􏰍􏰐e 􏰌f 􏰐􏰈􏰌ble􏰒.
Val􏰑e P􏰈􏰌􏰐􏰌􏰊i􏰎i􏰌n􏰊
Wha􏰎 a􏰈e 􏰏e 􏰎􏰈􏰍i􏰋g 􏰎􏰌 d􏰌 f􏰌􏰈 􏰎he e􏰋d-􏰑􏰊e􏰈(􏰊) 􏰌f 􏰎he 􏰐􏰈edic􏰎i􏰉e 􏰊􏰍􏰊􏰎e􏰒? Wha􏰎 􏰌bjec􏰎i􏰉e􏰊 a􏰈e 􏰏e 􏰊e􏰈􏰉i􏰋g?
Da􏰎a S􏰌􏰑􏰈ce􏰊
Which 􏰈a􏰏 da􏰎a 􏰊􏰌􏰑􏰈ce􏰊 ca􏰋 􏰏e 􏰑􏰊e (i􏰋􏰎e􏰈􏰋al a􏰋d e􏰓􏰎e􏰈􏰋al)?
C􏰌llec􏰎ing Da􏰎a
H􏰌􏰏 d􏰌 􏰏e ge􏰎 􏰋e􏰏 da􏰎a 􏰎􏰌 lea􏰈􏰋 f􏰈􏰌􏰒 (i􏰋􏰐􏰑􏰎􏰊 a􏰋d 􏰌􏰑􏰎􏰐􏰑􏰎􏰊)?
Making P􏰈edic􏰎i􏰌n􏰊
Whe􏰋 d􏰌 􏰏e 􏰒ake 􏰐􏰈edic􏰎i􏰌􏰋􏰊 􏰌􏰋 􏰋e􏰏 i􏰋􏰐􏰑􏰎􏰊? H􏰌􏰏 l􏰌􏰋g d􏰌 􏰏e ha􏰉e 􏰎􏰌 fea􏰎􏰑􏰈i􏰔e a 􏰋e􏰏 i􏰋􏰐􏰑􏰎 a􏰋d 􏰒ake a 􏰐􏰈edic􏰎i􏰌􏰋?
Offline E􏰉al􏰑a􏰎i􏰌n
Me􏰎h􏰌d􏰊 a􏰋d 􏰒e􏰎􏰈ic􏰊 􏰎􏰌 e􏰉al􏰑a􏰎e 􏰎he 􏰊􏰍􏰊􏰎e􏰒 bef􏰌􏰈e de􏰐l􏰌􏰍􏰒e􏰋􏰎.
Fea􏰎􏰑􏰈e􏰊
I􏰋􏰐􏰑􏰎 􏰈e􏰐􏰈e􏰊e􏰋􏰎a􏰎i􏰌􏰋􏰊 e􏰓􏰎􏰈ac􏰎ed f􏰈􏰌􏰒 􏰈a􏰏 da􏰎a 􏰊􏰌􏰑􏰈ce􏰊.
B􏰑ilding M􏰌del􏰊
Whe􏰋 d􏰌 􏰏e c􏰈ea􏰎e/􏰑􏰐da􏰎e
􏰒􏰌del􏰊 􏰏i􏰎h 􏰋e􏰏 􏰎􏰈ai􏰋i􏰋g
da􏰎a? H􏰌􏰏 l􏰌􏰋g d􏰌 􏰏e ha􏰉e 􏰎􏰌 fea􏰎􏰑􏰈i􏰔e 􏰎􏰈ai􏰋i􏰋g i􏰋􏰐􏰑􏰎􏰊 a􏰋d c􏰈ea􏰎e a 􏰒􏰌del?
Li􏰉e E􏰉al􏰑a􏰎i􏰌n and M􏰌ni􏰎􏰌􏰈ing
Me􏰎h􏰌d􏰊 a􏰋d 􏰒e􏰎􏰈ic􏰊 􏰎􏰌 e􏰉al􏰑a􏰎e 􏰎he 􏰊􏰍􏰊􏰎e􏰒 af􏰎e􏰈 de􏰐l􏰌􏰍􏰒e􏰋􏰎, a􏰋d 􏰎􏰌 􏰕􏰑a􏰋􏰎if􏰍 􏰉al􏰑e c􏰈ea􏰎i􏰌􏰋.
machinelea􏰈ningcan􏰉a􏰊.c􏰌m​ b􏰍 L􏰌􏰑i􏰊 D􏰌􏰈a􏰈d, Ph.D.
​Lice􏰋􏰊ed 􏰑􏰋de􏰈 a C􏰈ea􏰎i􏰉e C􏰌􏰒􏰒􏰌􏰋􏰊 A􏰎􏰎􏰈ib􏰑􏰎i􏰌􏰋-Sha􏰈eAlike 4.0 I􏰋􏰎e􏰈􏰋a􏰎i􏰌􏰋al Lice􏰋􏰊e.

HOMEWORK
Hands-on Machine Learning
Chapter 2: End-to-End Machine Learning Project
Try reading the Chapter from start to finish.We will work through the problem in class but please come prepared to discuss the case study.
It is easier to understand the different stages of a ML project if you follow one from start to finish.

PIPELINES

CALIFORNIA HOUSING DATASET HMLST – CHAPTER 2

DIS TRIBU TIONS

VISUALIZATION

CALIFORNIA HOUSING PRICES

END- TO-END
— Discover — Explore — Visualize
— Clean
— Sample — Impute — Encode — Transform
– Scale — Features — Pipelines
— Documentation — Presentation
— Launch — Monitor — Maintain
— Training/Validation splits — Modeling
— Tuning
— Error Analysis

ML PIPELINES
Source: https://epistasislab.github.io/tpot/

The Machine Lea􏰈ning Can􏰉a􏰊 (􏰉0.4)​ ​De􏰊ig􏰋ed f􏰌􏰈: ​ ​ De􏰊ig􏰋ed b􏰍: ​ ​ Da􏰎e: ​ ​ I􏰎e􏰈a􏰎i􏰌􏰋: ​ .
Deci􏰊i􏰌n􏰊
H􏰌􏰏 a􏰈e 􏰐􏰈edic􏰎i􏰌􏰋􏰊 􏰑􏰊ed 􏰎􏰌
􏰒ake deci􏰊i􏰌􏰋􏰊 􏰎ha􏰎 􏰐􏰈􏰌􏰉ide
􏰎he 􏰐􏰈􏰌􏰐􏰌􏰊ed 􏰉al􏰑e 􏰎􏰌 􏰎he e􏰋d-􏰑􏰊e􏰈?
ML 􏰎a􏰊k
I􏰋􏰐􏰑􏰎, 􏰌􏰑􏰎􏰐􏰑􏰎 􏰎􏰌 􏰐􏰈edic􏰎, 􏰎􏰍􏰐e 􏰌f 􏰐􏰈􏰌ble􏰒.
Val􏰑e P􏰈􏰌􏰐􏰌􏰊i􏰎i􏰌n􏰊
Wha􏰎 a􏰈e 􏰏e 􏰎􏰈􏰍i􏰋g 􏰎􏰌 d􏰌 f􏰌􏰈 􏰎he e􏰋d-􏰑􏰊e􏰈(􏰊) 􏰌f 􏰎he 􏰐􏰈edic􏰎i􏰉e 􏰊􏰍􏰊􏰎e􏰒? Wha􏰎 􏰌bjec􏰎i􏰉e􏰊 a􏰈e 􏰏e 􏰊e􏰈􏰉i􏰋g?
Da􏰎a S􏰌􏰑􏰈ce􏰊
Which 􏰈a􏰏 da􏰎a 􏰊􏰌􏰑􏰈ce􏰊 ca􏰋 􏰏e 􏰑􏰊e (i􏰋􏰎e􏰈􏰋al a􏰋d e􏰓􏰎e􏰈􏰋al)?
C􏰌llec􏰎ing Da􏰎a
H􏰌􏰏 d􏰌 􏰏e ge􏰎 􏰋e􏰏 da􏰎a 􏰎􏰌 lea􏰈􏰋 f􏰈􏰌􏰒 (i􏰋􏰐􏰑􏰎􏰊 a􏰋d 􏰌􏰑􏰎􏰐􏰑􏰎􏰊)?
Making P􏰈edic􏰎i􏰌n􏰊
Whe􏰋 d􏰌 􏰏e 􏰒ake 􏰐􏰈edic􏰎i􏰌􏰋􏰊 􏰌􏰋 􏰋e􏰏 i􏰋􏰐􏰑􏰎􏰊? H􏰌􏰏 l􏰌􏰋g d􏰌 􏰏e ha􏰉e 􏰎􏰌 fea􏰎􏰑􏰈i􏰔e a 􏰋e􏰏 i􏰋􏰐􏰑􏰎 a􏰋d 􏰒ake a 􏰐􏰈edic􏰎i􏰌􏰋?
Offline E􏰉al􏰑a􏰎i􏰌n
Me􏰎h􏰌d􏰊 a􏰋d 􏰒e􏰎􏰈ic􏰊 􏰎􏰌 e􏰉al􏰑a􏰎e 􏰎he 􏰊􏰍􏰊􏰎e􏰒 bef􏰌􏰈e de􏰐l􏰌􏰍􏰒e􏰋􏰎.
Fea􏰎􏰑􏰈e􏰊
I􏰋􏰐􏰑􏰎 􏰈e􏰐􏰈e􏰊e􏰋􏰎a􏰎i􏰌􏰋􏰊 e􏰓􏰎􏰈ac􏰎ed f􏰈􏰌􏰒 􏰈a􏰏 da􏰎a 􏰊􏰌􏰑􏰈ce􏰊.
B􏰑ilding M􏰌del􏰊
Whe􏰋 d􏰌 􏰏e c􏰈ea􏰎e/􏰑􏰐da􏰎e
􏰒􏰌del􏰊 􏰏i􏰎h 􏰋e􏰏 􏰎􏰈ai􏰋i􏰋g
da􏰎a? H􏰌􏰏 l􏰌􏰋g d􏰌 􏰏e ha􏰉e 􏰎􏰌 fea􏰎􏰑􏰈i􏰔e 􏰎􏰈ai􏰋i􏰋g i􏰋􏰐􏰑􏰎􏰊 a􏰋d c􏰈ea􏰎e a 􏰒􏰌del?
Li􏰉e E􏰉al􏰑a􏰎i􏰌n and M􏰌ni􏰎􏰌􏰈ing
Me􏰎h􏰌d􏰊 a􏰋d 􏰒e􏰎􏰈ic􏰊 􏰎􏰌 e􏰉al􏰑a􏰎e 􏰎he 􏰊􏰍􏰊􏰎e􏰒 af􏰎e􏰈 de􏰐l􏰌􏰍􏰒e􏰋􏰎, a􏰋d 􏰎􏰌 􏰕􏰑a􏰋􏰎if􏰍 􏰉al􏰑e c􏰈ea􏰎i􏰌􏰋.
machinelea􏰈ningcan􏰉a􏰊.c􏰌m​ b􏰍 L􏰌􏰑i􏰊 D􏰌􏰈a􏰈d, Ph.D. ​Lice􏰋􏰊ed 􏰑􏰋de􏰈 a C􏰈ea􏰎i􏰉e C􏰌􏰒􏰒􏰌􏰋􏰊 A􏰎􏰎􏰈ib􏰑􏰎i􏰌􏰋-Sha􏰈eAlike 4.0 I􏰋􏰎e􏰈􏰋a􏰎i􏰌􏰋al Lice􏰋􏰊e.

LECTURE 1 TERM 2:
MSIN0097
Predictive Analytics
A P MOORE