Machine Learning Fundamentals

This module introduces the fundamentals of machine learning and explores decision trees as a supervised learning method. Students learn about the core concepts of ML and how decision trees can be used for classification and prediction tasks.

Skills you'll gain

This module covers fundamental supervised learning techniques focusing on regression and classification methods. Students explore different approaches to predictive modeling and learn how to choose appropriate methods for various data types.

Skills you'll gain

This module explores the architecture and implementation of neural networks. Students learn about artificial neurons, network layers, backpropagation, and how neural networks can be applied to solve complex machine learning problems.

Skills you'll gain

This module focuses on instance-based learning methods where new instances are classified based on stored training instances. Students learn about nearest neighbor algorithms and memory-based learning approaches.

Skills you'll gain

This module covers ensemble learning techniques that combine multiple models to create more robust predictions. Students learn about bagging, boosting, and methods for combining different classifiers.

Skills you'll gain

This module explores kernel methods and Support Vector Machines (SVMs), teaching students about kernel functions, margin optimization, and how to apply SVMs to classification and regression problems.

Skills you'll gain

This module introduces the theoretical foundations of machine learning, covering computational learning theory and its implications for algorithm design and analysis.

Skills you'll gain

This module explores Vapnik-Chervonenkis (VC) theory and its importance in understanding the complexity and capabilities of learning algorithms. Students learn about measuring the capacity of learning algorithms.

Skills you'll gain

This module covers Bayesian learning methods, introducing students to probabilistic approaches in machine learning and how to apply Bayesian principles to learning algorithms.

Skills you'll gain

This module delves deeper into Bayesian inference techniques, teaching students about probabilistic reasoning, parameter estimation, and practical applications of Bayesian methods.

Skills you'll gain

This module introduces randomized optimization techniques used in machine learning, covering various algorithms for finding optimal solutions in complex search spaces.

Skills you'll gain

This module covers unsupervised learning techniques focusing on clustering algorithms and methods for selecting relevant features from datasets.

Skills you'll gain

This module explores feature transformation techniques and information theory principles in machine learning, teaching students about dimensionality reduction and information-theoretic approaches.

Skills you'll gain

This module introduces Markov Decision Processes (MDPs) as a framework for decision-making under uncertainty, covering state spaces, actions, and optimal policies.

Skills you'll gain

This module covers core concepts in reinforcement learning, teaching students about agents, environments, rewards, and algorithms for learning optimal behaviors through interaction.

Skills you'll gain

This module explores game theory in the context of machine learning and provides a comprehensive review of the course material, connecting various concepts covered throughout the course.

Skills you'll gain