MGMT 4190/6560 Introduction to Machine Learning Applications @Rensselaer

Welcome to Introduction to Machine Learning Applications

OVERVIEW

Schedule
Syllabus
Before Class
In Class
Assignments
The MS Business Analytics Capstone Course
Prior Examples
Faculty

SESSIONS

1. Course Overview & Introduction to the Data Science Lifecycle (08/31)
2. Python Basics (09/03)
3. Python Basics (First in Person Class, Tuesday follow Monday Schedule) (09/08)
4. Python conditionals, loops, functions, aggregating. (09/10)
5. Python conditionals, loops, functions, aggregating (continued) (09/14)
6. Python visualization, data manipulation , and feature creation. (09/17)
7. Python visualization, data manipulation , and feature creation (continued) (09/21)
8. Overview of Modeling (09/24)
9. Overview of Classification (09/28)
10. Overview of Classification (10/01)
11. Python and Regression (10/05)
12. Python and Regression (10/08)
13. Unsupervised Models (10/15)
14. Midterm Exam (10/19)
15. Time Series Analysis (10/22)
16. Time Series Analysis (10/26)
16. Time Series Analysis (10/26)
17. Text and NLP (10/29)
18. Text and NLP (11/02)
19. Introduction to Deep Learning (11/05)
19. Introduction to Deep Learning (11/05)
20. Introduction to Deep Learning (11/09)
21. Introduction to Deep Learning (11/12)
22. Image Data and Deep Learning (11/16)
23. NLP and Deep Learning (11/19)
24. R and Machine Learning (11/23)
25. Big Data (11/30)
26. Open project questions. (12/03)
27. Final Presentations (12/07)
28. Final Presentations (12/10)
29. Final Exam (12/15)

NOTEBOOKS

1. Overview of Python Features
2. Introduction Datastructures (Varibles, Lists, Dictionaries, and Sets)
3. Overview of Numpy
4. Introduction to Pandas
5. Conditional Statements and Loops
6. Functions
7. Null Values
8. Groupby and Pivot Tables
9. More Pivottables
10. Kaggle Baseline
11. Introduction to APIs
12. Web Mining
13. Introduction to Seaborn
14. String Manipulation and Regular Expressions
15. Feature Extraction
16. Feature Preprocessing with Scikit Learn
17. MatplotLab
18. Neural Networks and the Simplist XOR Problem
19. Train Test Splits
20. Classification with Scikit-learn
21. KNN
22. Linear Regression
23. Boston Housing
24. Lasso Ridge Regression
25. Regression with Stats-Models
26. Introduction to Principal Component Analysis
27. In Depth: Principal Component Analysis
28. k-Means Clustering
29. Coronavirus Data Modeling
30. Time Series Data
31. Panel Data vs Time Series Analysis
32. Basic Text Feature Creation in Python
33. Introduction to Text Mining in Python
34. Bag-of-Words Using Scikit Learn
35. What’s Cooking in Python
36. Bag of Words
37. IMDB
38. Neural Networks
39. Evaluation of Classifiers
40. Tensorflow Introduction
41. Tensorflow Tabular Data
42. Tensorflow NLP
43. Revisiting IRIS with PyTorch
44. PyTorch Deep Explainer MNIST example
45. PyTorch Deep Explainer MNIST example
46. Revisting Boston Housing with Pytorch
47. Titanic Fastai
48. Ludwig
49. Introduction to Map Reduce
50. Introduction to Spark

ASSIGNMENT STARTERS

Assignment 1
Assignment 2
Assignment 3
Assignment 4
Assignment 5
Assignment 6

Deep Learning

Hands On Machine Learning with Python
Fast.ai Book
Tensorflow Tutorials
Pytorch Tutorials
Kaggle Introduction to Deep Learning Course

IMPORTANT LINKS

RPI LMS
Webex Teams Discussion
Webex Teams Homework
Prof Kuruzovich Class Link
Box File link (Sec01)
Prof Morgan Class Link
Box File link (Sec02)

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Contents

29. Final Exam (12/15)¶

The final exam will be comprehensive. Tuesday, 12/15 11:30-2:30 Link

28. Final Presentations (12/10) 1. Overview of Python Features

By Jason Kuruzovich
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