~~~~ Machine Learning Course ~

PROJECT IN PROGRESS

Syllabus

Introduction
- What is Machine Learning?
- Supervised Learning
- Unsupervised Learning
[1 practice exercise]
Linear Regression with One Variable
- Model Representation
- Cost Function
- Gradient Descent
- Gradient Descent For Linear Regression
1. [1 practice exercise]
2. [Linear Regression I using Python]
3. [Cost Function using Python]
4. [Gradient Descent using Python]
Linear Algebra Review
- Matrices and Vectors
- Addition and Scalar Multiplication
- Matrix Vector Multiplication
- Matrix Matrix Multiplication
- Matrix Multiplication Properties
- Inverse and Transpose
1. [1 practice exercise]
2. [Python Programming]
Week 2

Linear Regression with Multiple Variables
- Multiple Features
- Gradient Descent for Multiple Variables
- Gradient Descent in Practice I - Feature Scaling
- Gradient Descent in Practice II - Learning Rate
- Features and Polynomial Regression
- Normal Equation
- Normal Equation Noninvertibility
- Working on and Submitting Programming Assignments
[1 practice exercise]
Octave/Matlab Tutorial
- Basic Operations
- Moving Data Around
- Computing on Data
- Plotting Data
- Control Statements: for, while, if statement
- Vectorization
[1 practice exercise]

Week 3

Logistic Regression
- Classification
- Hypothesis Representation
- Decision Boundary
- Cost Function
- Simplified Cost Function and Gradient Descent
- Advanced Optimization
- Multiclass Classification: One-vs-all
[1 practice exercise]
Regularization
- The Problem of Overfitting
- Cost Function
- Regularized Linear Regression
- Regularized Logistic Regression
[1 practice exercise]

Week 4

Neural Networks: Representation
- Non-linear Hypotheses
- Neurons and the Brain
- Model Representation I
- Model Representation II
- Examples and Intuitions I
- Examples and Intuitions II
- Multiclass Classification
[1 practice exercise]

Week 5

Neural Networks: Learning
- Cost Function
- Backpropagation Algorithm
- Backpropagation Intuition
- Implementation Note: Unrolling Parameter
- Gradient Checking
- Random Initialization
- Putting It Together
- Autonomous Driving
[1 practice exercise]

Week 6

Advice for Applying Machine Learning
- Deciding What to Try Next
- Evaluating a Hypothesis
- Model Selection and Train/Validation/Test Sets
- Diagnosing Bias vs. Variance
- Regularization and Bias/Variance
- Learning Curves
- Deciding What to Do Next Revisited
[1 practice exercise]
Machine Learning System Design
- Prioritizing What to Work On
- Error Analysis
- Error Metrics for Skewed Classes
- Trading Off Precision and Recall
- Data For Machine Learning
[1 practice exercise]

Week 7

Support Vector Machines
- Optimization Objective
- Large Margin Intuition
- Mathematics Behind Large Margin Classification
- Kernels I
- Kernels II
- Using An SVM
[1 practice exercise]

Week 8

Unsupervised Learning
- Unsupervised Learning: Introduction
- K-Means Algorithm
- Optimization Objective
- Random Initialization
- Choosing the Number of Clusters
[1 practice exercise]
Dimensionality Reduction
- Motivation I: Data Compression
- Motivation II: Visualization
- Principal Component Analysis Problem Formulation
- Principal Component Analysis Algorithm
- Reconstruction from Compressed Representation
- Choosing the Number of Principal Components
- Advice for Applying PCA
[1 practice exercise]

Week 9

Anomaly Detection
- Problem Motivation
- Gaussian Distribution
- Algorithm
- Developing and Evaluating an Anomaly Detection System
- Anomaly Detection vs. Supervised Learning
- Choosing What Features to Use
- Multivariate Gaussian Distribution
- Anomaly Detection using the Multivariate Gaussian Distribution
[1 practice exercise]
Recommender Systems
- Problem Formulation
- Content Based Recommendations
- Collaborative Filtering
- Collaborative Filtering Algorithm
- Vectorization: Low Rank Matrix Factorization
- Implementational Detail: Mean Normalization
[1 practice exercise]

Week 10

Large Scale Machine Learning
- Learning With Large Datasets
- Stochastic Gradient Descent
- Mini-Batch Gradient Descent
- Stochastic Gradient Descent Convergence
- Online Learning
- Map Reduce and Data Parallelism
[1 practice exercise]

Week 11

Application Example: Photo OCR
- Problem Description and Pipeline
- Sliding Windows
- Getting Lots of Data and Artificial Data
- Ceiling Analysis: What Part of the Pipeline to Work on Next
- Summary and Thank You
[1 practice exercise]