CURRICULUM
CLASSICAL MACHINE LEARNING
Part 1: k-Nearest Neighbors
Part 2: Linear Regression
Part 3: Logistic Regression
Part 4: Support Vector Machines
Part 5: Decision Trees
Part 6: Naive Bayes
This page is still under development
This page is still under development. Check back at a later time!
Linear Regression
Support Vector Machine
CAIS++
About
Projects
People
Current Members
Alumni
Invited Speakers
Curriculum
Overview
[1] Introduction to AI
[2] Classical Machine Learning
[2.1] k-Nearest Neighbors
[2.2] Linear Regression
[2.3] Logistic Regression
[2.4] Support Vector Machines
[2.5] Decision Trees
[2.6] Naive Bayes
[3] Neural Networks
[3.1] Architecture
[3.2] Training
[3.3] Optimization
[4] Neural Network Flavors
[4.1] Convolutional Neural Networks
[4.2] Recurrent Neural Networks
[4.3] Attention & Transformers
[4.4] Generative Adversarial Networks
[5] Special Topics
[5.1] Transfer Learning
[5.2] Reinforcement Learning
Reading List
Speaker Seminar
Apply
Contact Us
Contact Information
Our Sponsors
