Course Overview
This course provides a comprehensive introduction to key algorithms used in machine learning, a critical component of artificial intelligence. Students will explore fundamental techniques across multiple domains, including classification, regression, density estimation, dimensionality reduction, and clustering. Through this class, students will develop a robust understanding of the mathematical foundations, practical implementations, and applications of these algorithms.
Why AIClubProjects?
Opportunity to work on a wide range of software pipelines and research topics ranging from applications of AI in art and music to cybersecurity
Develop hands-on skills in absolute state of the art technology being used in the industry
Gain access to personalized mentoring from experienced researchers
Topics, Tools, and Modules:
1. Classification Techniques for categorizing data into classes:
Perceptrons
Support Vector Machines (SVMs)
Gaussian Discriminant Analysis (LDA and QDA)
Logistic Regression
Decision Trees
Neural Networks
Convolutional Neural Networks (CNNs)
Boosting
Nearest Neighbor Search
2. Regression Methods for predicting continuous outcomes:
Least-Squares Linear Regression
Logistic Regression
Polynomial Regression
Ridge Regression
Lasso
3. Density Estimation Approaches for modeling data distributions:
Maximum Likelihood Estimation (MLE)
4. Dimensionality Reduction Techniques for reducing data complexity while retaining essential information:
Principal Components Analysis (PCA)Random Projection
5. Clustering Methods for grouping similar data points:
k-Means Clustering Hierarchical Clustering
Learning Outcomes
By the end of the course, students will:
Understand and implement key machine learning algorithms.
Analyze their theoretical foundations and real-world applications.
Build practical models to solve diverse AI problems.
This course is suitable for students and professionals looking to deepen their understanding of machine learning algorithms and their role in artificial intelligence.