short_index.md

STA 663 Course Outline

• Lecture 1: Python and the IPython notebook (J)
• Lecture 2: Functional programming (C)
• Lecture 3: Python for data science (C)
• Lecture 4: Using numpy and pandas (C)
• Lecture 5: Computer arithmetic and algorithms (C)
• Lecture 6: Linear algebra and matrices I (J)
• Lecture 7: Linear algebra and matrices II (J)
• Lecture 8: PCA
• Lecture 9: Univariate optimization
• Lecture 10: Multivariate optimization
• Lecture 11: Constrained optimization
• Lecture 12: Expectation-Maximization I
• Lecture 13: Expectation-Maximization II
• Lecture 14: Random numbers and Monte Carlo methods
• Lecture 15: Computational inference and simulation experiments
• Lecture 16: MCMC I
• Lecture 17: MCMC II
• Lecture 18: Profiling and code optimization
• Lecture 19: Compiling to native code for speed
• Lecture 20: Parallel programming
• Lecture 21: GPU computing
• Lecture 22: MapReduce and SPARK