INTRODUCTION TO MACHINE LEARNING

Instructor: Elizer Ponio Jr

Email: [email protected]

Consultation Hours: Send me a message in MS Teams 🙂

📜 Course Description

This course introduces students with a broad variety of fundamental statistical-based algorithms used to train models for basic predictive tasks. This course also covers the theoretical and mathematical concepts of each method complemented by hands-on activities.

🏁 Pre-requisites for this class

• Proficiency in one programming language. All class assignments will be in Python. If you have a lot of programming experience but in a different language (e.g. Javascript/Java) you will probably be fine.
• College Calculus, Linear Algebra. You should be comfortable taking derivatives and understanding matrix vector operations and notation.
• Basic Probability and Statistics. You should know basics of probabilities, Gaussian distributions, mean, standard deviation, etc.

🚀 Learning Outcomes

By the end of the class students should be able to:

• Demonstrate sufficient knowledge in using various machine learning libraries and tools.
• Understand foundational algorithms used for model building for machine learning.
• Apply algorithms to a real-world problem using various machine learning libraries and tools, optimize the models learned, and report on the expected accuracy that can be achieved by applying the models.

📅 Course Outline and Timeframe

Course Syllabus

Expectations for Online Classes / Class Setup

Grading and Deadlines CAM

Class Setup for Machine Learning

Introduction to Google Colab and Jupyter Notebook

Basic Python Tutorial | | Google Colaboratory https://colab.research.google.com/

Get Started with Google Collab: https://www.youtube.com/watch?v=inN8seMm7UI | | | Week 2 | Introduction to the Course

History of AI and Machine Learning.

Artificial Intelligence vs. Machine Learning

Taxonomy of ML

Goals and Limitations of ML

Real world applications | | What is Artificial Intelligence?

https://youtu.be/mJeNghZXtMo | Exercise # 1 | | Week 3 | K Nearest Neighbors

Definition and Intuition

Hyperparameter (k)

Classification using k-NN

Applications | | K-Nearest Neighbors Demo (http://vision.stanford.edu/teaching/cs231n-demos/knn/) | Exercise # 2 | | Week 4 | Simple Linear Regression

Equation of a line

Cost function intuition

Parameters

Implementation | | An Introduction to Linear Regression Analysis: https://www.youtube.com/watch?v=NUXdtN1W1FE | Exercise # 3 | | Week 5 | Multiple Linear Regression

Model representation

Gradient descent for multiple variables

Feature scaling

Normal equation

Application | | Linear Regression with Multiple Variables

https://youtu.be/Q4GNLhRtZNc | Exercise # 4 | | Week 6 | Logistic Regression

Decision boundary for classification

Cost function and gradient descent for Logistic Regression

Multiclass classification | | | Exercise # 5 | | Week 7 | | | | Midterm Exam | | Week 8 | Naïve-Bayes Algorithm

Review of conditional probability

Bayes Rule

Independent events

Naïve Bayes for classification | | Visualized Naïve-Bayes: https://jakevdp.github.io/PythonDataScienceHandbook/05.05-naive-bayes.html | Exercise # 6 | | Week 9 | Support Vector Machines

The Hyperplane

Kernel method | | | Exercise # 7 | | Week 10 | Decision Trees | | | Exercise # 8 | | Week 11 | Ensemble Learning and Random Forests | | | Exercise # 9 | | Week 12 | | | | | | Week 13 | Course Project Submission | | | |

🏆 Grading

Exercises (50%)

Midterm Exam (10%)

Project (40%)