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Course Description:

This course is an explorative introduction into artificial intelligence, machine learning, deep learning, and its associated fields, concepts, and technologies. AI and its related fields are used in a profusion of diverse areas; from forecasting weather trends, business needs assessments, to medical/health analytics, computer/security surveillance and protection, farm science, energy production, urban planning, image and biometric analysis and recognition, as well as many other useful applications. The possibilities and utilizations of these burgeoning AI fields are limitless. Just as importantly, the employment opportunities are equally available and lucrative. The foci of this course will be on the current and relevant trends and applications in these fields, as well as the related skill sets. The skills, tools, and techniques learned in this course are relevant to careers in the aforementioned areas.

Course Objectives:

At the end of the course, the student will be able to:

  • Define terms and concepts used in AI and related fields;
  • Create individual development and distributed version control environments;
  • Identify characteristics of various intelligent systems;
  • Evaluate AI strategies used to address specific practical real-world applications;
  • Apply AI techniques on data sets at a basic level.

Course Work Links:

  1. Assignment 1: Version Control and Development Installation - A1 README.md
    • Version Control Setup and Installations
      • Setting up Git on remote and home workstations
      • Installing R
      • Installing Anaconda
      • Jupyter Notebooks Setup
    • A1 Assignment - First Python Assignment
      • Answering Questions
      • A1 Tip Calculaor, written in Python
      • Create a Jupyter Notebook or A1TC
  2. Assignment 2: Backwards Engineering and Jupyter Notebooks - A2 README.md
    • Reverse Engineer Python content
      • Post results in Juypter Notebooks enivronment
    • Screenshots of resulting code in Jupyter Notebooks
    • Assignment Questions
  3. Assignment 3 -More Work with DataFrames in PythonA3 README.md
    • Reverse engineer python content
      • Post results in a Jupyter Notebooks
    • Make some sick graphs
    • Post a GIF file of it running in IPYNB -Assignment Questions
  4. Project 1 - Predictive Analysis (Simple Linear Regression) P1 README.md
    • Reverse engineer python content
      • Post results in a Jupyter Notebooks
    • Learning Linear Regression!
    • Building a model using housing data
    • Starting work with Seaborn
    • Make some sick graphs
    • Post a GIF file of it running in IPYNB -Assignment Questions
  5. Assignment 4 - Predictive Analysis (SLR)
    • Contrast similarities/differences among AI vs. Machine-Learning
    • Idenitfy Correlations
    • Use Seaborn (data)
    • Graph correlations
    • Use simple linear regression
    • Create linear model
    • Plot regression line
    • Make predictions - using simple regression model
    • Pick residuals
  6. Assignment 5 - Predictive Analysis (MLR README.md
    • Predictive Analysis
    • Analyze Data
    • Identify Correlations
    • Create multiple regression model
    • Find best line to predict output
  7. P2 README.md