CURRICULUM

Students will be using Microsoft Azure ML Studio, with real data-sets, for class exercises.

Please bring own Laptop to class.

Basic


Foundational concepts in AI and ML. Be sure to know Algebra 1 (Current enrollment is ok).


  • Motivation for Artificial Intelligence
  • Types of Learning: 
    Supervised, Unsupervised
  • Understanding types of AI Problems and exercises in each: 
    Classification, Anomaly Detection, Regressions, Clustering
  • Building Predictive Models (regression and Classification): Understanding hypothesis and cost functions, Model metrics & Optimizing Cost Functions
  • Exercises in: House prices, Diabetes prediction, Handwriting recognition, Titanic survival etc.
  • Final Class Project (team based)
  • Review and Wrap-up

Intermediate

Learn the Tools of the Trade - More advanced problem solving. Be sure to have familiarity with basics course content and basic proficiency with a programming language.

  • Intro to Neural Networks
  • Review of Algebra - Matrix Math with Python
  • Python for Machine Learning
  • Support Vector Machines
  • Recommender Systems
  • Principal Component Analysis (PCA)
  • Decision Trees
  • Ensemble Learning and Random Forests
  • Tensorflow
  • AI Hardware (CPU, GPU, TPU, ML Accelerators..)
  • Development: Image Recognition using Tensorflow, Image Compression with PCA
  • Introduction to Kaggle Competitions
Contact

Advanced

Real Life Exercises; Showcase Work; Establish Credibility in AI Community. Be sure to have familiarity with machine learning concepts & tools and basic proficiency with a programming language.

  • Review and Wrap-up
  • Training with large Data Sets
  • ebugging AI implementation
  • Real life Data Sets
  • Using GPUs for training
  • Development: Medical Diagnosis, Autonomous Car
  • Kaggle Competitions
  • Invited speakers from AI community
Contact