Posts about Coursera
- Advanced Optimization
- Advice for Applying PCA
- Algorithm
- Anomaly Detection vs Supervised Learning
- Autonomous Driving (Examples)
- Backpropagation Algorithm
- Backpropagation Intuition
- Ceiling Analysis: What Part of the Pipeline to Work on Next
- Choosing the Number of Principal Components
- Choosing what features to use
- Collaborative filtering
- Collaborative filtering algorithm
- Content-based Recommendation
- Cost Function
- Data for Machine Learning
- Deciding What to Do Next (Revisited)
- Deciding what to Try Next
- Developing and evaluating an anomaly detection system
- Diagnosing bias vs. variance
- Error Analysis
- Error metrics for Skewed Classes
- Evaluating a hyphotesis
- Examples & Intuition l
- Examples & Intuition ll
- Features And Polynomial Regression
- Gaussian Distribution
- Getting Lots of Data and Artificial Data Synthesis
- Gradient Checking
- Gradient Descent
- Implementation detail: Mean Normalization
- Implementation note: Unrolling parameters
- Introduction
- K-means algorithm
- Kernels I
- Kernels II
- Large Margin Intuition
- Learning Curves
- Learning With Large Datasets
- Map-reduce and data-parallelism
- Mini Batch Gradient Descent
- Model Representation
- Model Representation l
- Model Representation ll
- Model selection and training/validation/test sets
- Motivation I : Data Compression
- Motivation II : Data Visualization
- Multi-class Classification
- multiclass classification
- Multiple Variables
- Neat Tricks
- Neurons & the brain
- Non-linear hypothesis
- Normal Equation
- Online Learning
- Optimization Objective
- Principal Component Analysis Algorithm
- Principal Component Analysis problem formulation
- Prioritizing What to Work On
- Problem Description and Pipeline
- Problem Formulation
- Problem Motivation
- Putting it together
- Random Initialization
- Reconstruction from compressed representation
- Regularization and Bias/Variance
- Regularized Linear Regression
- Regularized Logistic Regression
- Server Computers (AD Ex.)
- Simplified cost function and gradient descent
- Sliding Windows
- Stochastic Gradient Descent
- Stochastic Gradient Descent Convergence
- Summary
- The problem of overfitting
- Trading of Precision & Recall
- Unsupervised Learning: Introduction
- Using SVM
- Vectorization: Low Rank Matrix Factorization
- Frequentist vs. Bayesian Approach
- Inference and Diagnostics for MLR
- Multiple Linear Regression
- Conditions and Inference of Linear Regression
- Introduction to Linear Regression
- Comparing categorical proportions and Chi-Square
- Hypothesis Testing and Confidence Interval for Categorical Variable
- t-distribution and ANOVA
- Paired data and Bootstrapping
- Decision Hypothesis Testing and Confidence Interval
- Hypothesis Testing
- Confidence Interval
- Introduction to Statistical Distributions
- Intro to Inferential Statistics
- Exploratory Data Analysis
- Design Study