WELCOME ALL!

A new quarter begins and new knowledge awaits! Welcome to Machine Learning (COMP 486). Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Linear Regression, Regularized Linear Models, Logistic Regression, Linear SVM Classification,Decision Trees,Random Forests, Artificial Neural Networks.

By the end of the quarter you should learn how to analyze and identify significant characteristics of data sets, develop an understanding of training a learning algorithm including over-fitting, noise, convergence and stopping criteria, match a data set with the most promising inductive learning algorithms, understand and implement the training, testing, and validation phases of learning algorithms,development and deployment, and apply machine learning algorithms for classification and functional approximation or regression. Use the syllabus to learn more about the class or the menu bar in the top to explore the course content.

One last thing: