Resources

 Machine Learning Course

Machine learning (ML) enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML techniques are widely applied in engineering, science, finance, and commerce to build systems for which we do not have full mathematical specification (and that covers a lot of systems). The course balances theory and practice, and covers the mathematical as well as the heuristic aspects.

Machine Learning Course Video Playlist

Jan 16 2016

 Introduction to Algorithms

MIT 6.006, Fall 2011 This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

MIT Course Algorithms Video Playlist

Jan 16 2016

 Programming Abstractions

Julie Zelenski gives an introduction to the course, recursion, algorithms, dynamic data structures and data abstraction; she also introduced the significance of programming and gives her opinion of what makes 106B "great;" C++ is introduced, too.

Stanford Course Abstraction Video Playlist

Feb 16 2016