Lecture Slides Fall 2020 all slides
Lectures: Reference https://www.coursera.org/learn/machine-learning/
Lec_1 Course Overview
Lec_2 Probability and Statistics for Data Science – 1
Lec3 Probability and Statistics for Data Science – 1
Lec4 Probability and Statistics for Data Science – 1
Lec5,7 Probability and Statistics for Data Science – 1
Lec8 Midterm exam
Lec9 Data Visualization: just an overview
Lec10 Machine Learning: introduction & linear regression
Recommended textbook:
1- Mathematical Statistics and Data Analysis John A. Rice
University of California, Berkeley
2. Practical Statistics for Data Scientists, 50 Essential Concepts, Peter Bruce and Andrew Bruce
3. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. https://web.stanford.edu/~hastie/ElemStatLearn/
4. An Introduction to Statistical Learning with Applications in R http://faculty.marshall.usc.edu/gareth-james/ISL/
5. Probability & Statistics for Engineers & Scientists, Ronald E. Walpole Roanoke College Raymond H. Myers Virginia Tech Sharon L. Myers Radford University Keying Ye University of Texas at San Antonio. http://www.fisica.edu.uy/~gallardo/tem/Walpole.pdf
In-depth introduction to machine learning in 15 hours of expert videos https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
Introduction to Probability and Data
https://www.coursera.org/learn/probability-intro
Introduction to Probability, Statistics, and Random Processes
https://www.probabilitycourse.com/
useful problems & solutions
Example 5.2, 5.3, 5.4 3.2.5. solved problems (1, 2 , 3, 4, 9)
Example 3.12, 3.14, 3.16
N.B. no need to go in derivative steps.