CURRICULUM

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

Please bring own Laptop to class.

Basics

AB (Middle Schoolers) & BC (High Schoolers)


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, 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)

Basics AB focuses on middle school students. Covers all above topics with less emphasis on mathematics behind them but more emphasis on applications and exercises.

Basics BC is focused on high school students and will cover mathematics behind AI/ML modeling with few advanced topics in modeling and data cleaning.

Both AB ad BC students continue to intermediate class.

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
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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
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