Special Topics

DATE	AGENDA	DUE
1/15	Topic: Course introduction. Introduction to Artificial Intelligence Topic: Introduction to Artificial Intelligence (continued)
1/22	Topic: How Artificial Intelligence, Data Science, Machine Learning and Software Engineering interconnect Required reading MIS_3580_002 – Artificial_Intelligence_Project1 In-class activity: Introduction to Microsoft Power BI Required viewing YouTube: Guy in a cube – Power BI Tutorial: How to Sign up Microsoft: What is PowerBI? YouTube: What is PowerBI? Microsoft: PowerBI Demo Required reading Wikipedia: Fact Tables Wikipedia: Dimension Tables Microsoft: PowerBI Self Service Sign-up The StudentsPerformance datset from Kaggle
1/29	Topic: Types of software systems. Business rules versus learning based systems In-class activity: Building a rules based system. Your first PowerBI dashboard Required reading Microsoft: Connect to csv files in PowerBI Microsoft: Add visuals to a Power BI report (part 1) Microsoft: Add visuals to a PowerBI Report (part 2)
2/05	Topic: How does AI work? In-class activity: Building an AI based system. Add (artificial) intelligence to your PowerBI dashboard Required viewing YouTube: Guy in a cube – AI features of PowerBi YouTube: Guy in a cube – Key Influencers visual
2/12	Topic: Choosing between an AI or a Business Rules based solution Topic: Cloud Infrastructures. Introducing Microsoft Azure cloud Required viewing YouTube: What is Microsoft Azure? YouTube: How does Azure Work?	Project – Part 1: Instructions
2/19	Topic: How does Machine Learning work? In-class activity: Explore the Machine Learning Workspace Required reading Wikipedia : Machine Learning ML Mastery : Test vs. Validation data sets Azure ML Overview Azure Automated ML Azure AutoML tutorial
2/26	EXAM #1 – MID TERM EXAM
3/04	Spring break
3/11	Topic: Data Science and Machine Learning – Part 1 In-class activity: Build a training data set
3/18	Topic: Data Science and Machine Learning – Part 2 In-class activity: Training and scoring a predictive model I Required reading: ML Pitfalls Towards data science : Over-fitting Wikipedia: Over-fitting Measuring model performance
3/25	In-class activity: Training and scoring a predictive model II In-class activity: Training and scoring a predictive model III
4/01	In-class activity: Training and scoring a predictive model IV In-class activity: Training and scoring a predictive model V
4/08	Topic: Introduction to Bots In-class activity: Building a knowledge base I
4/15	In-class activity: Building a knowledge base II In-class activity: Training and testing a Bot I
4/22	In-class activity: Training and testing a bot II Class wrap-up and next steps
4/29	EXAM #2 – FINAL EXAM	Project – Part 2: Instructions and rubric

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