12-weeks course that provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include:
- Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
- Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
Also the course provides a draw from numerous case studies and applications, where it is possible to learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
The following assignments are done in Matlab/Octave.
Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and you have data for profits and populations from the cities.
Suppose you are selling your house and you want to know what a good market price would be. One way to do this is to first collect information on recent houses sold and make a model of housing prices. The file ex1data2.txt contains a training set of housing prices in Portland, Oregon. The first column is the size of the house (in square feet), the second column is the number of bedrooms, and the third column is the price of the house.
Content:
- Visualization
- Gradient Descent
- Cost Function
- Feature Normalization
Suppose that you are the administrator of a university department and you want to determine each applicant’s chance of admission based on their results on two exams. You have historical data from previous applicants that you can use as a training set for logistic regression. For each training example, you have the applicant’s scores on two exams and the admissions decision. The task is to build a classification model that estimates an applicant’s probability of admission based on the scores from those two exams.
Content:
- Visualization
- Cost Function using Sigmoid function
- Regularized logistic regression
- Feature mapping (to fit the data better into polynomial terms)
- Plot non linear decision boundaries
Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks.
Content:
- One vs all classification
- Unrolled vectors
- Feedforward Propagation
Implementation of the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition.
Content:
- Backpropagation
- Gradient checking
- Regularized Neural Network
Regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir.
Content:
- Bias vs Variance
- Fitting Linear Regression
- Polynomial Regression
Content:
- Nonlinear classification using SVM with Gaussian Kernels
- Normalization of values (HTML, URL, email addresses, numbers, dollars)
- Word Stemming
- Removal of non-words and punctuation
Content:
- Implementing K-means (Find closest centroid, computing centroid means)
- Implementing PCA: 2D to 1D, and 3D to 2D
Application of this algorithm to detect failing servers on a network
Build a collaborative filtering learning algorithm to build a recommender system for movies based on users ranking from 1 to 5.
Content:
- Differences between Anomaly Detection and Supervised learning
- Use of Multivariate Gaussian Distribution as a Anomaly Detection Algorithm
- Recommender Systems Algorithms using Linear Regression Cost Function and Gradient Descent