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Theoretical Foundations of Data Science I

DSC 40A, Spring 2024 at UC San Diego

Suraj Rampure
he/him

rampure@ucsd.edu

Lecture(s): TuTh 12:30-1:50PM (A), 2-3:20PM (B), Center Hall 212

Jump to the current week Assignment Solutions

Check out the new 🫂 Advice page, which contains tips on how to succeed in DSC 40A from current tutors, based on their experiences in the course.
Also, note that there is no live lecture on Tuesday, May 28th, and instead, a pre-recorded lecture video will be posted on Tuesday morning.

Week 1 – Modeling and Loss Functions
📘 Read Note 1, Pages 1-12.

Tue Apr 2

LEC 1 Introduction to Modeling
                       

SUR Welcome Survey

Thu Apr 4

LEC 2 Empirical Risk Minimization
                       

EX HW Example Homework (not due!)
                       

Week 2 – Simple Linear Regression
📘 Read the spread notes and Note 2, Pages 1-7.

Mon Apr 8

DISC 1 Groupwork 1
                  

Tue Apr 9

LEC 3 Comparing Loss Functions
                       

Thu Apr 11

LEC 4 Simple Linear Regression
                           

HW 1 Homework 1
                     

Week 3 – Linear Algebra Review
📘 Read Note 2, Pages 7-13 and take a look at the Week 2 Lecture FAQs.

Mon Apr 15

DISC 2 Groupwork 2
                     

Tue Apr 16

LEC 5 Simple Linear Regression, Continued
                         

Thu Apr 18

LEC 6 Dot Products and Projections
                       

HW 2 Homework 2
                     

Week 4 – Regression and Linear Algebra
📘 Read Note 2, Pages 10-19.

Mon Apr 22

DISC 3 Groupwork 3
                     

Tue Apr 23

LEC 7 Orthogonal Projections
                       

Thu Apr 25

LEC 8 Regression and Linear Algebra
                         

Sat Apr 27

HW 3 Homework 3 (note the later deadline!)
                     

Week 5 – Multiple Linear Regression, Feature Engineering
📘 Read Note 1, Pages 16-17.

Mon Apr 29

DISC 4 Groupwork 4
                     

Tue Apr 30

LEC 9 Multiple Linear Regression
                         

Thu May 2

LEC 10 Feature Engineering, Gradient Descent
                           

HW 4 Homework 4
                     

Fri May 3

REV Midterm Review Session (Center Hall 109, 2-5PM)
                       

Week 6 – Midterm Exam, Gradient Descent
📘 Optionally, see these notes on convexity.

Mon May 6

DISC 5 Midterm Exam Review (no worksheet, no required attendance)

Tue May 7

EXAM Midterm Exam (in person, during assigned lecture)

Thu May 9

LEC 11 Gradient Descent, Continued
                         

Week 7 – Probability
📘 Read Janine's probability roadmap and Chapters 1 and 2 of this probability textbook.

Mon May 13

DISC 6 Groupwork 5
                     

Tue May 14

LEC 12 Foundations of Probability
                       

Thu May 16

LEC 13 Combinatorics
                           

HW 5 Homework 5
                     

Week 8 – Combinatorics, Independence

Mon May 20

DISC 7 Groupwork 6
                     

Tue May 21

LEC 14 More Combinatorics Examples
                       

Thu May 23

LEC 15 Bayes' Theorem and Independence
                       

HW 6 Homework 6
                     

Week 9 – Naïve Bayes
There will not be live lecture on Tuesday. Instead, lecture will be pre-recorded and posted on Tuesday morning. Along with Tuesday's lecture, read this note on conditional independence.

Mon May 27

DISC 8 Groupwork 7

Tue May 28

LEC 16 Naïve Bayes

Thu May 30

LEC 17 Naïve Bayes, Continued

HW 7 Homework 7

Week 10 – More Classifiers, Conclusion

Mon Jun 3

DISC 9 Groupwork 8

Tue Jun 4

LEC 18 Classification

Thu Jun 6

LEC 19 Review, Conclusion

HW 8 Homework 8

Sat Jun 8

EXAM Final Exam (8-11AM, in person, location TBD)