Introduction to Machine Learning

Introduction to Machine Learning


This course will provide a fundamental understanding of machine learning principles and models including supervised learning, linear regression, logistic regression, unsupervised learning, k-means, decision trees and support vector machines as well as the demonstration of how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to finance applications.

We will use the Jupyter Notebook environment for coding and build our models using the scikit-learn library.


The course is organized in a lecture-recitation format. This is the planned schedule for the whole course, but of course, changes are possible due to unforeseen reasons.
The mapping between lectures and course sessions is not one to one. Meaning that we might work on several lectures in one course session or working on one lecture for several sessions.

Lecture # Title Lecture Topics Recitations Externals
1 Introduction to Artificial Intelligence AI in business Python programming language
2 Introduction to Machine Learning What is machine learning?
Machine learning algorithms
Supervised learning
Unsupervised Learning
Reinforcement Learning
Pandas Library
3 Supervised learning: Regression Model representation and cost function
Linear regression
Gradient descent
Regression hands-on practice
4 Supervised learning: Classification Binary classification
Multi-class classification
Logistic regression
K nearest neighbours (KNN)
Support vector machines (SVM)
Decision trees
Ensemble methods and random forest
Classification hands-on practice
5 Overfitting Bias-variance trade-off
Cross validation
Overfitting in decision trees
6 Model Evaluation | Model Selection Evaluation metrics (case of regression)
Evaluation metrics (case of classification)
Unbalanced datasets
Model evaluations
7 Unsupervised Learning K-means clustering
Hierarchical clustering
DBSCAN clustering
Find optimal number of clusters
Clustering practice
8 Data Exploration Data visualization in Python Final project
Saeed Varasteh Yazdi

Saeed Varasteh Yazdi

Assistant Professor, Machine Learning Researcher

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