Machine Learning Program

Course Description:
Machine Learning as a field is now incredibly pervasive, with applications spanning from business intelligence to homeland security, from analyzing biochemical interactions to structural monitoring of aging bridges, and from emissions to astrophysics, etc. Deep learning is a branch of machine learning concerned with the development and application of modern neural networks. Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance of a give task. Deep learning is behind many recent advances in AI, including Siri’s speech recognition, Facebook’s tag suggestions, machine language translation and self-driving cars. This course is an introduction to Machine Learning using TensorFlow 2.0, which is a very popular framework for building predictive models. The course will provide a step by step approach to building complex machine learning models starting from the very basics concepts of machine learning and the TensorFlow 2.0 framework from Google. We will be using a variety of tools and platforms such as Python, TensorFlow/Keras, and Google Collaboratory Notebooks for building, testing, and deploying machine learning models.

The 12-week, 72-hour program covers fundamental topics exposing students to Artificial Intelligence and Machine Learning. The program is ideal for graduating and working engineers new to the Artificial Intelligence and Machine Learning world.

This module contains specializations for Retail, Healthcare, Financial Services and Industrial / Manufacturing. You can select one or more specializations as part of the course (each specialization is 3-5 weeks long). You will understand the use cases defined below and implement one use case end-to-end as a part of your project.

A PACE Certificate of Achievement will be awarded upon successful completion of the program.

Learning Outcomes
By the end of the course, students will be able to: 

  • Explain how machine learning models work
  • Frame tasks into machine learning problems
  • Use machine learning toolkits to implement the designed models
  • Justify when and why specific machine learning techniques work for specific problems
  • Build, test, and deploy complex machine learning models to solve specific problems

Tentative Course Outline

Each module contains corresponding hands-on labs covering module topics.

Module 1: Python Basics - 3 weeks

  • Python Tutorial, including:
    • Data Types & Strings
    • User Defined Functions
    • Pandas Series
    • Lambda & Map
    • Introduction to Classes and Objects

Module 2: Machine Learning using TensorFlow - 4 weeks

  • Introduction to Machine Learning
  • What is Machine Learning?
  • Introduction to TensorFlow
  • Building TensorFlow Models
  • Scaling-up and Model Deployment

Module 3: Industry Focus - 5 weeks

  • Retail
  • Healthcare
  • Financial Services
  • Industrial/Manufacturing

Instructor Information
Name: Vijayan Sugumaran
Title: Distinguished Professor of Management Information Systems
Contact Information: sugumara@oakland.edu

Name: Naresh Jasotani
Title: Specialist Customer Engg. (AI / ML, Data & Analytics)
Contact Information: nareshjasotani@google.com

Textbooks and Materials 
Adopting TensorFlow for Real-World AI: A Practical Approach-TensorFlow v2.2 by Naresh R. Jasotani, 2020, ISBN: 9798643487456. (Paper back copy provided by the program)

Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python, by Pramod Singh and Avinash Manure, Apress, 2020, ISBN: 978-1-4842-5560-5
(A soft copy is available on the course Moodle Page. It is referred to as PS in the syllabus)