Data 140: Probability for Data Science

UC Berkeley, Spring 2025

Week 1

Jan 19

• Welcome to Data 140! The first lecture will be on Tuesday January 21st, from 2 PM - 3:30 PM in Dwinelle 155. The first sections will happen on Wednesday, January 22nd.
• Please carefully read through the course information, which covers the details of the course this fall.
• Please work through the math prerequisites. The first lecture will use Basic Counting, Sums, and Exponential and Log Functions.

Calendar

Jump to current week

Week 1: Introduction

Jan 20

Holiday

Jan 21

Guide Week 1

Lecture Probability: axioms, rules, approximation

Course Information, Ch 1, 2

Homework Homework 1

Due Jan 27 at 5 PM

Lab Lab 1

Due Jan 27 at 5 PM

Jan 22

Section

Jan 23

Lecture Random variables: distributions, equality, conditioning

Ch 3, 4

Jan 24

Mega-Section

Week 2: Symmetry and Random Counts

Jan 27

Jan 28

Lecture Symmetry and collections of events

Ch 5

Jan 29

Section

Jan 30

Lecture Binomial and related counts; a Poisson limit

Ch 6

Jan 31

Mega-Section

Week 3: Poissonization and Expectation

Feb 3

Feb 4

Lecture Independence and Poissonization

Ch 7

Feb 5

Section

Feb 6

Lecture Expectation

Ch 8.1 - 8.3

Feb 7

Mega-Section

Week 4: Expectation and Conditioning

Feb 10

Feb 11

Lecture Expectation and additivity

Ch 8.4 - 8.5

Feb 12

Section

Feb 13

Lecture Expectation by conditioning

Ch 9

Feb 14

Mega-Section

Week 5: Markov Chains

Feb 17

Holiday

Feb 18

Lecture Long run behavior of Markov chains

Ch 10

Feb 19

Exam Midterm 1

8PM - 10PM

Feb 20

Lecture Balance

Ch 10, 11.1

Feb 21

Mega-Section

Week 6: MCMC and Variance

Feb 24

Feb 25

Lecture Markov Chain Monte Carlo

Ch 11.2 - 11.3

Feb 26

Section

Feb 27

Lecture Standard deviation and tail bounds

Ch 12

Feb 28

Mega-Section

Week 7: Covariance and CLT

Mar 3

Mar 4

Lecture Covariance and its uses

Ch 13

Mar 5

Section

Mar 6

Lecture Central Limit Theorem

Ch 14

Mar 7

Mega-Section

Week 8: Densities and Transformations

Mar 10

Mar 11

Lecture Probability densities

Ch 15

Mar 12

Section

Mar 13

Lecture Transformations

Ch 16

Mar 14

Mega-Section

Week 9: Joint Densities, Beta, Normal, and Gamma Families

Mar 17

Mar 18

Lecture Joint densities

Ch 17

Mar 19

Section

Mar 20

Lecture Joint distributions; sums of normal and gamma variables

Ch 17, Ch 18

Mar 21

Mega-Section

Spring Break

Mar 24

Spring Break

Mar 25

Spring Break

Mar 26

Spring Break

Mar 27

Spring Break

Mar 28

Spring Break

Week 10: MGFs

Mar 31

Apr 1

Lecture Moment generating functions

Ch 19

Apr 2

Exam Midterm 2

8PM - 10PM

Apr 3

Lecture Moment generating functions; Chernoff bound

Ch 19

Apr 4

Mega-Section

Week 11: MLE, MAP, the Beta and the Binomial

Apr 7

Apr 8

Lecture MLE; conditioning; MAP estimates

Ch 20

Apr 9

Section

Apr 10

Lecture The beta and the binomial

Ch 21

Apr 11

Mega-Section

Week 12: Prediction and Error

Apr 14

Apr 15

Lecture Prediction and error

Ch 22.1 - 22.2

Apr 16

Section

Apr 17

Lecture Variance by conditioning

Ch 22.3 - 22.4

Apr 18

Mega-Section

Week 13: Random Vectors and Simple Regression

Apr 21

Apr 22

Lecture Random vectors; the multivariate normal

Ch 23

Apr 23

Section

Apr 24

Lecture Correlation and simple regression

Ch 24

Apr 25

Mega-Section

Week 14: Multiple Regression

Apr 28

Apr 29

Lecture Multiple regression I

Ch 25.1 - 25.3

Apr 30

Section

May 1

Lecture Multiple regression II

Ch 25.4

May 2

Mega-Section

Week 15: Reading, Review, Recitation

May 5

RRR Week

May 6

RRR Week

May 7

RRR Week

May 8

RRR Week

May 9

RRR Week

Week 16: Final Exams

May 12

Exam Final Exam

11:30AM - 2:30PM