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Codes are implemented in both matlab and python.
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Each week has its own summary readme. (check each folder)
Week 1
- Class: Machine learning definition, Supervised and Unsupervised Learning, Model representation, Cost function, Gradient Descent, Gradient Descent For Linear Regression.
Week 2
- Class: Multivariate Linear Regression, Gradient Descent For Multiple Variables, Feature Scaling & Mean normalization, Learning Rate, Features and Polynomial Regression, Normal Equation.
- Homework: Implementing Linear Regression, Gradient Descent.
Week 3
- Class: Classification, Hypothesis representation of classification problems (Sigmoid function), Decision Boundary, Logistic Regression Model (Cost function + Gradient descent), Multiclass Classification, Overfitting, Underfitting, Regularization, Regularization in linear and logistic regression.
- Homework: Using logistic regression for prediciting whether a student gets admitted into a university.
Week 4
- Class: Non-liner Hyphothesis, Neural Networks Model representation, Multiclass classification, Forward propagation.
- Homework: one-vs all logistic regression and neural networks to recognize handwritten digits.
Week 5
- Class: Neural network cost function, Back propagation algorithm, Gradient checking, Random initialization for weights, Symmetry breaking.
- Homework: backpropagation of recognizing handwritten digits.
Week 6
- Class: Improve machine learning algorithms, Evaluating a hypothesis, Model selection and cross validation set, Dignoising bias vs variance, Learning curves, Precision and Recall, Fscore.
- Homework: Regularized linear regression and use it to study models with different bias-variance properties.
Week 7
- Class: Support Vector Machine, SVM decision boundry, Kernels, Gaussian kernel and Linear kernel, Landmarks, SVM parameters, multi-class classification SVM.
- Homework: using support vector machines (SVMs) to build a spam classifier.
Week 8
- Class: Clustering, K-means Algorithm, Dimensionaly Reduction, PCA, Reconstruction from compressed Representation.
- Homework: K-means clustering algorithm and apply it to compress an image.
Week 9
- Class: Anomaly detection, Guassian distribution, Density estimation, Parameter estimation, Multivariate gaussian distribution, Recommender Systems, Collaborative filtering
- Homework: Anomaly detection algorithm and apply it to detect failing servers on a network.
Week 10
- Class: Batch Gradient Descent, Stochastic Gradient Descent, Stochastic Gradient Descent Convergence, Mini Batch Gradient Descent, Online Learning, Map reduce and Data Parallelism.
Week 11
- Class: Photo OCR, Artificial data synthesis, Sliding window, Ceiling analysis.