—— SOLUTIONS —— May 2019 1. a. Write down Bayes’ Rule, giving an expression for the posterior, P (ωj |x), in terms of the likelihood, prior and evidence. Where ωj represents category j, and x is a sample. Marking scheme 3 marks. Copyright By PowCoder代写 加微信 powcoder P(ωj|x) = p(x|ωj)P(ωj) p(x) b. Briefly describe what […]
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COMP9417 Project COMP9417 Project June 21, 2021 Aims Learning objectives of this assignment: I a self-selected task to extend aspects of the course material I involves practical aspects of the machine learning problem, i.e. I implementing or modifying algorithms and/or I experimental evaluation of algorithms on data set(s) I exercise written communication skills in motivating,
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MFIN 290 Application of Machine Learning in Finance: Lecture 5 MFIN 290 Application of Machine Learning in Finance: Lecture 5 Edward Sheng 7/24/2021 Agenda Review of Homework 1 Release of Final Project Review of Lecture 1 – 4 Midterm Exam (1 hour) 1 2 3 4 2 Section 1: Review of Homework 1 3 Section
MFIN 290 Application of Machine Learning in Finance: Lecture 9 MFIN 290 Application of Machine Learning in Finance: Lecture 9 Edward Sheng, Yujie He 8/21/2021 Agenda Review of Homework 3 Review of Mid-term Review of Lecture 1 – 4 and 7 – 8 Mock interview on key concepts 1 2 3 4 2 Section 1:
MFIN 290 Application of Machine Learning in Finance: Lecture 2 MFIN 290 Application of Machine Learning in Finance: Lecture 2 Edward Sheng 7/5/2021 Agenda Basic Decision Tree Support Vector Machine (SVM) 1 2 3 2 Bagging and Boosting Tree Section 1: Basic Decision Tree 3 Moving beyond linearity From linear to non-linear Linear models are
PowerPoint 演示文稿 MFIN 290 Application of Machine Learning in Finance: Lecture 3 Yujie He 7/10/2021 Background of lecturer Tech Lead/Senior Applied Scientist in Microsoft Multiple patents and conference papers in knowledge graph and natural language processing (NLP) Expertise in deep learning, NLP and building end-to-end AI systems Work with applied machine learning in various scenarios
Stratopoulos & Vanden Bosch, Waterloo – 2021 Analytic Methods for Business – I Introduction to Data Analytics Process: Spreadsheets and R Theophanis C. Stratopoulos1 Nancy Vanden Bosch November 21, 2021 1Contact author: Theophanis C. Stratopoulos PhD, School of Accounting and Finance – University of Waterloo, Waterloo ON N2L 3G1, Canada. Stratopoulos & Vanden Bosch, Waterloo
Stratopoulos & Vanden Bosch, Waterloo – 2021 Analytic Methods for Business – I Introduction to Data Analytics Process: Spreadsheets and R Theophanis C. Stratopoulos1 Nancy Vanden Bosch November 14, 2021 1Contact author: Theophanis C. Stratopoulos PhD, School of Accounting and Finance – University of Waterloo, Waterloo ON N2L 3G1, Canada. Stratopoulos & Vanden Bosch, Waterloo
PowerPoint Presentation Re-cap from Week 8 – Decision Tree Classification and regression tree (continuous and binary variables) The logic of decision-tree algorithm (information entropy; information gain) Model evaluation (Lift, Misclassification, ROC) Model comparison Learning Objectives Describe Automated Machine Learning (AML) Explain why AML is different from traditional data science development Describe the benefits to the
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PowerPoint Presentation Recap from Week 7- Logistic Regression Logistic Regression Model (Probability, Odds, Logit) Model Evaluation (Confusion Matrix, Fit, Lift, ROC) Model Predication (interpret results for categorical variables, making prediction ) Decision Trees Learning Objectives The learning outcomes from this week’s lecture are: Explain how a decision tree works for categorical and continuous outcome variables
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