Intro to Machine Learning

[AI 1]

Full Course

$1906 USD
Before any discounts or coupons
for 18 hours

Class Description:

[AI 1] is a math heavy course offered at KTBYTE, and require students to have mastered independently following up on lecture topics and self-teaching outside of class. Students will learn tools to model and understand complex data sets, tools and algorithms that are commonly used for tackling "Big Data" problems. Covered topics include different techniques in supervised learning, unsupervised learning and reinforcement learning. This course is taught in Python using the pandas, numpy, and sk-Learn libraries. Students will have roughly 2 hours of homework assignments per week, plus a final project due at the end of the semester.

[AI 1] vs Core classes:
[AI 1] provides the theoretical and mathematical foundations to understand learning, and students do regular problem sets. The goal is to derive and understand the actual equations of various models. This includes techniques such as clustering, linear regression, and naive bayes. For many KTBYTE students, [AI 1] is also the first time they program using python. Unlike core classes, students are not taught python 'from the ground up', and are expected to pick up the language as it is used with examples in class.

Research Projects from KTBYTE students and alumni

Prerequisites:

Completion of [CORE 6a] or AP CS, or permission of instructor. Also requires Algebra II math experience.

Related Classes

Syllabus

Working With Data: Finding Statistics

Importing data sets and finding statistics

Working with Data: Slicing and Indexing

Slicing and indexing data sets

Classification: Types of Problems and Models

What types of problems and models exist in machine learning? What do most models have in common?

Regression: Linear Regression

Linear Regression and Feature Importance

Regression: Common Regression Problems

Types of regression models and what each one is typically used for.

Regression: Gradient Descent

How does gradient descent work, and how can we use it to optimize our models?

Classification: Logistic Regression

Logistic regression

Classification: Decision Trees

Decision trees and feature importance

Classification: More Decision Trees and Random Forest

More on decision trees and using ensemble methods to improve performance

Clustering

Clustering models and unsupervised learning

Cross Validation

Train test split AUC score, accuracy / precision / recall

Research Project

Finding/starting a project

Research Project

Finding and starting a project

Research Project

Related Works + Experiment Design

Research Project

Results

Research Project

Writing, and related works

Research Project

Writing, Introduction and Abstract

Research Project

Finishing the Research Project