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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

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

Thu May 23

LEC 15 Independence and Bayes' Theorem

HW 6 Homework 6
                     

Week 9 – Naïve Bayes

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)