Course Description: This course provides a comprehensive introduction to machine learning, focusing on the principles and techniques used to develop algorithms that can learn from and make predictions on data. Students will explore a variety of machine learning models and methods, gaining hands-on experience with their applications in diverse domains.
Key Topics Covered:
Introduction to Machine Learning: Overview of machine learning concepts, types of learning (supervised, unsupervised, and reinforcement learning).
Data Preprocessing: Techniques for cleaning, transforming, and preparing data for analysis.
Supervised Learning: Algorithms for classification and regression, including linear regression, decision trees, and support vector machines.
Unsupervised Learning: Methods for clustering and dimensionality reduction, such as k-means clustering and principal component analysis (PCA).
Model Evaluation: Techniques for assessing model performance and tuning hyperparameters.
Deep Learning: Fundamentals of neural networks and deep learning frameworks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
Prerequisites: Basic knowledge of programming, statistics, and linear algebra.
Course Format: Lectures, hands-on labs, and project work.
Link to Instructional Media and Materials: