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CSE/MB604 Machine Learning Fundamental

This course offers essential elements of traditional Machine Learning with a focused introduction to supervised and unsupervised learning algorithms, statistical modeling, and key best practice techniques for building well trained models.
  • » 23 hours (8 weeks) in class lecturing plus dedicated mentoring sessions from our faculty of industry experts
  • » 1.5 semester credits for both certificate and master’s degree
  • » Access to high-quality live class recording
  • » Online live classroom available for all classes
  • » Lifetime learning resources for our students
  • ETTP Program $7200
Course Description

This course offers essential elements of traditional Machine Learning with a focused introduction to supervised and unsupervised learning algorithms, statistical modeling, and key best practice techniques for building well trained models.

Reference

  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
  • by Aurélien Géron
  • Publisher: O'Reilly Media; 2 edition (September 5, 2019)
  • ISBN-13: 978-1492032649
  • ISBN-10: 1492032646
Learning Resource Center

Students will be required to use the library for study. The librarian or library assistant is available to students to provide assistance and guidance on navigating the online resources of the Learning Resource Center. CSTU website has provide details instructions on how to access the Learning Resource Center and search for articles in a specific field.

Course Objectives
  • Demonstrate a basic understanding of machine learning
  • Describe the major concepts of machine learning, its main framework derived from traditional data analytics
  • Demonstrate ability to build model, train model, tune ML models
Weekly Schedule
Week 1-4
  • Simple models
    • Introduction to ML
    • K-Nearest neighbors
    • Dimensionality reduction
    • Naïve Bayes
  • Basic regression
    • Cross-validation and Quality Metrics
    • Regularized Linear Regression
    • Regularized Logistic Regression
    • Evaluation Metrics
Week 5-6
  • Linear Models
    • SVMs
    • Tuning linear models
  • Forests
    • Decision trees
    • Random forests
  • Bagging and Boosting
    • Bagging on standard classifiers
    • Adaptive Boosting (Adaboost)
    • Gradient Boosting
Week 7-8
  • Unsupervised learning
    • Introduction
    • K-means algorithm
    • Expectation-maximization (GMMs)
    • Proximity clustering
    • Cluster quality
  • Gaussian Mixtures
    • Anomaly detection using Gaussian mixtures
    • Selecting the number of clusters
    • Bayesian Gaussian Mixtures
    • Other algorithms for Anomaly detection
About the Instructor

Amir Emadzadeh

Dr. Emadzadeh is a senior software engineer. He is very enthusiastic about robots/autonomous agents. He has been recently working on localization of self-driving cars. Self-driven and detailed oriented problem solver. Highly motivated to build innovation ecosystems. Cross-functional skills.

Skills: Leadership/People management, Stochastic estimation, Machine learning, Digital signal processing, Controls, Linux, C++, Python, Matlab