Ed Saiedi | Teaching

To watch my guide on how to set-up a budget office/home studio for online presentations (used for my Machine Learning course at BI), please watch this video:

Ed Saiedi | teaching

BI Norwegian Business School

Predictive Analytics with Machine Learning

Spring 2021: Course Instructor

GRA4136

Lecture 1a: Introduction to Predictive Analytics
Lecture 1b: Types of Analytics and Predictive Analytics

slides video
Lecture 2: Preprocessing, Data Encoding and Reduction
Lecture 3: Regression 1 - Types, Bias/Variance, Overfit Avoidance
Lecture 4: Regression 2 - Train/Test Split vs. Cross-validation
Lecture 5: Unsupervised Learning
Lecture 6: Classification 1 - Trees
Lecture 7: Classification 2 - Trees (Cont.)
Lecture 8: Classification 3 - SVM
Lecture 9: Classification 4 - k-Nearest Neighbours
Lecture 10: Classification 5 - ROC Curve, Unbalanced Classes, DataRobot Classification
Lecture 11: DataRobot Evaluation and Model Valuation
Lecture 12: Automated Machine Learning, Implementation and Deployment

Previous Teaching

(2016,2017,2018; Co-lecturing): ME2706: Basic Econometrics Royal Institute of Technology (KTH)with Ingrid Viklund-Ros and Luis Perez
(2019; Guest Lecturing): ME2066: Strategy and Industrial Marketing Royal Institute of Technology (KTH)
(2018; Guest Lecturing): 8096 - From Science to Business Stockholm School of Entrepreneurship (SSES)
(2018; Guest Lecturing): FE5826 - Advanced Quantitative Methods in Accounting & Operations Management Stockholm Business School (SBS)
(2015; Guest Lecturing): IMIM: Corporate Ethics and Environment Technical University of Madrid (UPM)