Machine Learning

The Motivation & Applications of Machine Learning
Andrew Ng

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This module introduces the motivation behind machine learning and its various applications across industries. Students will learn about the class logistics and the fundamental definitions of machine learning.

It provides an overview of:

Supervised Learning

Learning Theory

Unsupervised Learning

Reinforcement Learning
An Application of Supervised Learning - Autonomous Deriving
Andrew Ng

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This module focuses on the application of supervised learning in the context of autonomous driving. Key topics include:

ALVINN

Linear Regression

Gradient Descent Techniques

Matrix Derivative Notation

Derivation of Normal Equations

Students will gain hands-on experience with these concepts, applying them to real-world scenarios.
The Concept of Underfitting and Overfitting
Andrew Ng

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This module delves into the concepts of underfitting and overfitting, critical aspects of model performance in machine learning. Topics include:

Parametric and Non-parametric Algorithms

Locally Weighted Regression

Probabilistic Interpretation of Linear Regression

Logistic Regression and Perceptron

Students will learn to identify and address these issues to improve model accuracy.
Newton's Method
Andrew Ng

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This module introduces Newton's Method, a powerful optimization algorithm used in machine learning. Students will explore:

Exponential Family Distributions

Bernoulli and Gaussian Examples

General Linear Models (GLMs)

Softmax Regression

Practical examples will illustrate how these concepts are applied in various machine learning scenarios.
Discriminative Algorithms
Andrew Ng

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This module covers discriminative algorithms, contrasting them with generative algorithms. Key topics include:

Gaussian Discriminant Analysis (GDA)

Relationship between GDA and Logistic Regression

Naive Bayes Classifiers

Laplace Smoothing Technique

Students will understand how to implement these algorithms in various contexts.
Multinomial Event Model
Andrew Ng

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This module explores the Multinomial Event Model, focusing on non-linear classifiers and neural networks. Topics covered include:

Intuitive Understanding of Support Vector Machines (SVM)

Notation for SVM

Functional and Geometric Margins

Students will learn how these concepts apply to real-world data problems.
Optimal Margin Classifier
Andrew Ng

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This module focuses on the Optimal Margin Classifier, introducing students to advanced concepts such as:

Lagrange Duality

Karush-Kuhn-Tucker (KKT) Conditions

SVM Dual

The Concept of Kernels

Practical exercises will help students understand these advanced theoretical concepts.
Kernels
Andrew Ng

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In this module, students will gain insights into Kernels and their role in creating non-linear decision boundaries. Key topics include:

Mercer's Theorem

Soft Margin SVM

Coordinate Ascent Algorithm

Sequential Minimization Optimization (SMO) Algorithm

Applications of SVM

This module combines theory with practical applications to show how kernels enhance SVMs.
Bias/variance Tradeoff
Andrew Ng

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This module addresses the Bias/Variance Tradeoff, which is essential for understanding model performance. Key topics include:

Empirical Risk Minimization (ERM)

The Union Bound

Hoeffding Inequality

Uniform Convergence - Finite Hypothesis Class

Sample Complexity Bound and Error Bound

In-depth discussions will help students grasp the implications of bias and variance in model development.
Uniform Convergence - The Case of Infinite H
Andrew Ng

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This module extends the discussion of Uniform Convergence to cases with infinite hypothesis classes. Topics covered include:

The Concept of "Shatter" and VC Dimension

SVM Examples

Model Selection

Cross Validation and Feature Selection

Students will learn how these concepts relate to creating robust machine learning models.
Bayesian Statistics and Regularization
Andrew Ng

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This module discusses Bayesian Statistics and Regularization techniques in the context of machine learning. Key topics include:

Online Learning Techniques

Advice for Applying Machine Learning Algorithms

Debugging and Fixing Learning Algorithms

Diagnostics for Bias & Variance

Error Analysis and Getting Started on Learning Problems

Students will gain practical insights into how to apply Bayesian methods effectively.
The Concept of Unsupervised Learning
Andrew Ng

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This module introduces the concept of Unsupervised Learning, covering key techniques like:

K-means Clustering Algorithm

Mixtures of Gaussians and the EM Algorithm

Jensen's Inequality

The EM Algorithm

Students will engage in practical exercises to understand how these techniques are applied in real-world data analysis.
Mixture of Gaussian
Andrew Ng

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This module focuses on the Mixture of Gaussian models and their applications, including:

Mixture of Naive Bayes for Text Clustering

Factor Analysis

Restrictions on a Covariance Matrix

EM for Factor Analysis

Students will learn how to implement and interpret these models in various contexts.
The Factor Analysis Model
Andrew Ng

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This module discusses the Factor Analysis Model and techniques for dimensionality reduction. Key topics include:

EM for Factor Analysis

Principal Component Analysis (PCA)

PCA as a Dimensionality Reduction Algorithm

Applications of PCA, including Face Recognition

Engagement in practical applications will illustrate the importance of PCA in data analysis.
Latent Semantic Indexing (LSI)
Andrew Ng

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This module covers Latent Semantic Indexing (LSI) and its mathematical foundations. Topics include:

Singular Value Decomposition (SVD) Implementation

Independent Component Analysis (ICA)

The Application of ICA

Cumulative Distribution Function (CDF) and the ICA Algorithm

Students will learn about LSI's role in information retrieval and text analysis.
Applications of Reinforcement Learning
Andrew Ng

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This module explores the Applications of Reinforcement Learning, focusing on key concepts such as:

Markov Decision Process (MDP)

Defining Value & Policy Functions

Optimal Value Function

Value Iteration and Policy Iteration

Students will learn how reinforcement learning can be applied to complex decision-making scenarios.
Generalization to Continuous States
Andrew Ng

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This module addresses the Generalization to Continuous States, crucial for developing robust reinforcement learning algorithms. Topics include:

Discretization and the Curse of Dimensionality

Models and Simulators

Fitted Value Iteration

Finding Optimal Policy

Students will engage in exercises to apply these concepts in developing effective models.
State-action Rewards
Andrew Ng

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This module covers State-action Rewards in reinforcement learning and introduces concepts such as:

Finite Horizon MDPs

Dynamical Systems

Examples of Dynamical Models

Linear Quadratic Regulation (LQR)

Computing Rewards and Riccati Equation

Students will learn how to apply these concepts to real-world decision-making scenarios.
Advice for Applying Machine Learning
Andrew Ng

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This module offers practical advice for applying machine learning effectively, focusing on:

Debugging Reinforcement Learning Algorithms

Linear Quadratic Regularization (LQR)

Differential Dynamic Programming (DDP)

Kalman Filter & Linear Quadratic Gaussian (LQG)

Predict/Update Steps of Kalman Filter

Students will engage in practical exercises to reinforce learning and application.
Partially Observable MDPs (POMDPs)
Andrew Ng

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This module introduces Partially Observable MDPs (POMDPs) and discusses their significance, covering:

Policy Search Techniques

Reinforce Algorithm

Pegasus Algorithm

Applications of Reinforcement Learning

Students will learn about the challenges and strategies associated with POMDPs in complex environments.

Course

Course Lectures