My first blog post

Hello,

My name is Dmitry and I have set target to improve my statistics and machine learning skills.

Throughout the year I will be completing different MOOCs from Carnegie Mellon UC, participate in machine learning competitions and contribute to Julia Lang. You will have a chance to follow my progress directly here.

Carnegie Mellon UC provides access to Syllabus and course materials. On top of that I was lucky to find a page describing the full path becoming a Machine Learning Expert.

I will start with basics Calculus 1, 21-111. The textbook for this course is Brief Calculus & Its Applications by Larry J. Goldstein, David C. Lay, and David I. Schneider.

Major Requirements
Theory Requirements
Course Topic/Title Course Number Units Prerequisites
Calculus 21-111 and 112, or 21-120 20 or 10
Integration and Approximation 21-122 10 21-112 or 21-120
Multivariate Calc/Analysis 21-256, 21-259, or 21-268  9–10 21-112 or 21-120
Concepts of Mathematics 21-127 10
Linear/Matrix Algebra 21-240, 21-241, or 21-242 10
Probability 36-217, 21-325, 15-359, or 36-225  9 21-112, 21-122, 21-123, 21-256, or 21-259
Statistical Inference 36-226 or 36-326  9 C or higher in 36-217, 36-225, 21-325, or 15-359
Data-Analysis Requirements (Option 1)
Course Topic/Title Course Number Units Prerequisites
Beginning Data Analysis 36-201, 36-220, or 36-247  9
Intermediate Data Analysis 36-202, 36-208, or 36-309  9 various
Advanced Elective 36-315, 36-303, 36-490, or 36-46x  9 various
Advanced Elective 36-315, 36-303, 36-490, or 36-46x  9 various
Modern Regression 36-401  9 C or higher in 36-226, 36-326, or 36-625 and pass 21-240 or 21-241
Advanced Methods for Data Analysis 36-402  9 C or higher in 36-401
Data-Analysis Requirements (Option 2)
Course Topic/Title Course Number Units Prerequisites
Advanced Elective 36-315, 36-303, 36-490, or 36-46x  9 various
Advanced Elective 36-315, 36-303, 36-490, or 36-46x  9 various
Advanced Elective 36-315, 36-303, 36-490, or 36-46x  9 various
Modern Regression 36-401  9 C or higher in 36-226, 36-326, or 36-625 and pass 21-240 or 21-241
Advanced Methods for Data Analysis 36-402  9 C or higher in 36-401
Computing Requirements
Course Topic/Title Course Number Units Prerequisites
Statistical Computing 36-350 or 36-650/750  9 36-202, 36-208, 36-309, 70-208, or equivalent
Fundamentals of Programming 15-112 12
Principles of Iterative Computation 15-122 10 C or higher in 15-112
Machine Learning 10-401/601/701 12 C or higher in (15-122 or 15-123) and (15-151 or 21-127) and (36-217 or 36-225 or 21-325 or 15-359)
Algorithms and Advanced Data Structures 15-351/451 12 15-111, 15-123, 15-121, or 15-122
Large Data Sets 10-405/605 or Advanced
Machine Learning Elective
(10-605, 15-381, 15-386, 16-720, 16-311, 11-411, or 11-761)

Fingers crossed and lets get started!

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