Lecture Slides Fall 2020 all slides
Lectures: Reference https://www.coursera.org/learn/machine-learning/

Lecture Slides Fall 2019

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

Useful related online courses :

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

Introduction to Probability, Statistics, and Random Processes
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.