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DS106 Machine Learning and Modeling

Course Description: The Machine Learning and Modeling course will introduce students to several commonly used machine learning methods. Students will learn how to determine the best methods for a given set of data and how to use common software tools to utilize these methods.
Quarter Credit Hours: 4.5
Course Length: 60 hours
Prerequisites: DS102, DS108, DS109
Proficiency Exam: No
Theory Hours: 30
Laboratory Hours: 30
Externship Hours: 0
Outside Hours: 15
Total Contact Hours: 60
Module Lesson Number Lesson Name
DS106 Modeling 1 Modeling with Linear Regression
2 Modeling with Logistic Regression
3 Modeling with Non-Linear and Multiple Regression
4 Modeling with Step-Wise Regression
5 Randomly Generated Data
DS106 Machine Learning 1 Introduction to Machine Learning
2 K-Means and K-Nearest Neighbors
3 Decision Trees and Random Forests
4 Bayesian Networks
5 Natural Language Processing

Required Resources:

  • Ground-based students are required to bring a late model laptop computer (either PC or MacBook) to class every day.

  • Online students are required to have a late model laptop or desktop computer with internet access.

  • Minimum: PC (Windows 10/11) or Mac (Big Sur or Monterey) laptop. 8GB ram, 512GB HD, Intel Core i5, AMD Ryzen 5, or Apple Intel or M1 Chipsets.

  • Recommended: PC (Windows 10/11) or Mac laptop(Big Sur or Monterey). 16GB ram, 1TB SSD, Intel Core i7, AMD Ryzen 7, or Apple M1/M1 Pro Chipsets.

  • Professionals: PC (Windows 10/11) or Mac(Big Sur or Monterey). 32-64 GB ram, 2-8TB SSD, Intel Core i9, AMD Ryzen 9/Threadripper, or Apple M1 Max Chipsets.

  • It is a requirement that you are able to download programming resources to your laptop/desktop for this class. (This means you need a steady internet high bandwidth connection.)

  • You are required to have a quiet place to study and to be able to focus on the material.

  • You are required to have uninterrupted weekly 1:1 video meetings with your mentor.

  • You are required to log into the Learning Management System (LMS) daily for at least 20 minutes.

  • Please follow and review each lesson page by page coding examples provided as this will ensure you have a full understanding for your final hands-on assignments.

Educational Objectives:

Upon successful completion of this course, students will be able to:

  1. Use common machine learning techniques:

    a. Decision Trees b. Random Forests c. Clustering d. Supervised and unsupervised learning e. Bayesian Networks

  2. Use common modeling techniques:

    a. Simple linear regression b. Non-linear regression c. Logistic regression d. Step-wise regression e. Using simulation to predict future events and reduce risk

Points

Lesson Points
L1 Hands On 19 points
L2 Hands On 19 points
L3 Practice Hands On 0 points
L4 Practice Hands On 0 points
L5 Hands On 19 points
L6 Practice Hands On 0 points
L7 Hands On 19 points
L8 Hands On 19 points
L9 Practice Hands On 0 points
L10 Hands On 19 points

Final Project:

Using Python, create a decision tree model of the Titanic dataset from seaborn. Interpret the confusion matrix and classification report.

The student is required to run two different simulations: a Monte Carlo to predict profit of a certain tool, and an expensive piece of equipment that needs to limit inventory. Create a table with the information requested.

Course Evaluation Strategies (Methodologies):

  • Professionalism, Attendance and Class Participation* 5%
  • Assignments/Hands-On/Homework 95%
  • Total 100%