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Executive Education
Clariden Leadership Institute
Predictive Analytics And Data Forecasting With Machine Learning (Melbourne)
Philippe Touati
Belmond Capital
Philippe Touati, Belmond Capital
  • Experienced Finance and Banking leader with over 30 years of experience in the banking and telecommunication industry
  • Former Head of Institutional Banking for ANZ and Managing Director/Co-head of Standard Chartered Bank Singapore, Wholesale Bank
  • Regular speaker and trainer in Deep Tech, Advanced Data Analytics and AI-based digital transformation frameworks

Philippe builds and finances AI ventures offering automation, optimization, personalization, and virtualization solutions to enable enterprises’ digital transformation. Previously, he was Head of Institutional Banking at ANZ and MD/Co-Head of the $1billion revenue Wholesale Bank at Standard Chartered Bank Singapore. His financial services experience also includes running Capital One Bank’s European operations functions.


Before financial services, Philippe held senior leadership positions in telecoms and technology where he designed a VLSI integrated circuit at Bell Labs, launched operations of New T&T, a HK telecom start-up, ran IT and operations for ICO, a satellite MEO company and managed 5 operation centres at France Telecom.


Philippe has a Master of Science in Mathematics from Ecole Polytechnique as well as Masters of Science in Electrical Engineering from both Telecom ParisTech and Columbia University.




Philippe’s course has broadened my appreciation for AI. It gave me some keywords that I can drop in meetings to make me sound good!"

I have learnt about some new techniques and ways on how they can be applied. Also heard from the group on the sort of problems they face and suggestions from Philippe on how they can be solved."

Program Summary

Traditional forecasting systems uses predetermined rules to analyze data. Machine learning, however, enables the system to adapt and dynamically respond to data changes, and update forecast based on discovery of hidden relationships between variables. It also enables the combination of millions of data points from many disparate sources to create comprehensive forecasts that far exceed human capabilities. This program will show you how to use machine learning to build predictive models by extracting patterns from large datasets, minimize any forecasting errors and improve your predictive modelling to uncover business insights in real time. 


Companies such as P&G and Uber have reduced supply chain inventory forecasting errors by 50% and improved financial forecasting accuracy by 99% using machine learning.


Programs, dates and locations are subject to change. In accordance with Clariden Global policy, we do not discriminate against any person on the basis of race, color, sex, religion, age, national or disability in admission to our programs.


Today’s leaders understand the power of decision-making based on data collected in the past and modelled for the future. Proper understanding of data modelling and forecasting can make you an invaluable asset for your organization. 


Our Advanced Data Analytics and AI transformation expert, Mr. Philippe Touati will walk you through key models and concepts in data modelling and forecasting. You will see when to use these different forecasting tools, the key mechanisms behind them and how to build a strategic framework for maintaining data integrity, compliance, usability, availability and security.


By the end of this course, you will walk away with practical guidance on applying AI & machine learning to solve real business problems - backed by lessons from four practical case studies covering regression applied to house price, time series analysis for sales & demand forecasting, unsupervised learning for classification and a combined approach for business applications such as customer profitability and supply chain management.

What You Can Expect

Key Benefits

  • Discover how to improve forecasting based on supervised machine learning
  • Prepare your data for machine learning work with python
  • Get insights on using machine learning to uncover hidden relationships between variables
  • Avoid bias in machine learning models
  • Apply machine learning and forecasting across industries including supply chain, financial forecast, customer profitability, lead scoring, fraud detection and predictive maintenance
  • Learn how to audit your data model, improve governance and control

Who Will Benefit Most

Executives, Managers, Directors, Head of Departments and C-Suites in (but not limited to) data science & engineering, solution architects, business intelligence and analytics, information technology, artificial intelligence and software development, as well as practitioners involved in product planning & operations and business & finance strategy and economy.


Program Outline

Day 1


Session 1: Enhancing Business Performance With Data Modelling And Forecasting

  • Are Data Models Only For Data Scientists?
  • Data Modelling As A Source Of Competitive Advantage In A Data-driven World
  • Types Of Forecasting: Regression And Classification


Case Study 1: Housing Price Forecasting: Non-Model Vs Model Based Regression Analysis 

  • Instance Based Forecasting: Knn-k Nearest Neighbours
  • Is A Data Model Just A Glorified Excel Spreadsheet?
  • Multivariate Linear Regression - Supervised Machine Learning For Data Model- Based Forecasting
  • Choosing The Right Metrics - Rmse


Session 2: Data Modelling Forecasting Process

  • Components Of A Data Model
  • Statistical Distribution, Mathematical Optimizer And Ml-based Model
  • Selecting Data Model For Generalization: Bias And Variance Trade-off, Overfitting


Session 3: Business Intelligence Is Not Data Modelling Or Forecasting

  • Smart Reporting 
  • Predictive And Predictive Analytics


Session 4: Introduction To Time Series Regression Forecasting Based On Supervised Machine Learning

  • Moving Average Using Excel And Python-based Notebook
  • Exponential Smoothing
  • Autoregressive Model
  • Acf And Pacf Times Series Reviews


Case Study 2: Time Series Forecasting - Demand And Sales Forecasting

  • Practical (S)Arima
  • Choosing The Right Metrics - Rmse/Aic
  • Testing Data Model For Generalization


Day 2


Session 5: Supervised Machine Learning Data Modelling Workflow And Tools

  • Etl Process
  • Data Representation And Features Selection/Extraction
  • Curse Of Data Dimensionality
  • Auto Ml Platforms / Forecasting Point Solutions


Session 6: Classification Forecasting Based On Supervised Machine Learning

  • Supervised Learning Steps To Build The Right Classification Models
  • Logistic Regression
  • Decision Tree, Boosted Tree And Random Forest
  • Ensemble Approach
  • Loss Function Metrics And X-entropy


Case Study 3: Classification Forecasting - Predictive Maintenance /Lead Scoring /Fraud Detection

  • Class Imbalance Challenges 
  • Data Model/Algorithm Comparison And Selection
  • Assessing With The Right Metrics: Precision/Recall/Accuracy/Auc


Case Study 4: Complex Multi-step Forecasting Combining Various Data Modelling Approaches 

  • Forecasting Customer Profitability 
  • Supply Chain Inventory Management


Session 7: Data Modelling Governance In A Data Driven Decision-making World

  • Data Model Iterations And Approval Matrix
  • Data Model Explainability, Control And Audit
  • Fairness And Bias Challenges 
  • Traceability Of Data Versions


Session 8: How to Kickstart Data Model Based Forecasting in Your Organization



CFOs Leadership :
Experience Clariden
Discover how our leadership program has shaped the perspectives of CFOs across Asia
Venue: InterContinental Melbourne
Date: 23 - 24 March 2020
Faculty: Philippe Touati
Early Bird 1: AU$2,395 (by 7 Feb 2020)
Early Bird 2: AU$2,595 (by 6 Mar 2020)
Regular Fee: AU$2,695
Group Discount: 2nd participant get 10%, or register 3 participants and 4th participant get a complimentary seat
(1 discount scheme applies)
Contact: [email protected]
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