Theoretical Foundations of Data Science I
DSC 40A, Spring 2024 at UC San Diego
Suraj Rampurehe/him
Lecture(s): TuTh 12:30-1:50PM (A), 2-3:20PM (B), Center Hall 212
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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
Week 2 – Simple Linear Regression
📘 Read the spread notes and Note 2, Pages 1-7.
Week 3 – Regression and Linear Algebra
📘 Read Note 2, Pages 7-13 and take a look at the Week 2 Lecture FAQs.
Week 4 – Multiple Linear Regression
📘 Read Note 2, Pages 10-19.
- Mon Apr 22
DISC 3 Groupwork 3
- Tue Apr 23
LEC 7 Regression and Linear Algebra, Continued
- Thu Apr 25
LEC 8 Multiple Linear Regression and Feature Engineering
HW 3 Homework 3
Week 5 – Gradient Descent and Convexity
📘 Read Note 1, Pages 16-17.
- Mon Apr 29
DISC 4 Groupwork 4
- Tue Apr 30
LEC 9 Gradient Descent and Convexity
- Thu May 2
LEC 10 Gradient Descent in Multiple Dimensions
HW 4 Homework 4
Week 6 – Midterm Exam, Clustering
- Mon May 6
DISC 5 Groupwork 5
- Tue May 7
EXAM Midterm Exam (in person, during assigned lecture)
- Thu May 9
LEC 11 Clustering
HW 5 Homework 5
Week 7 – Probability
- Mon May 13
DISC 6 Groupwork 6
- Tue May 14
LEC 12 Rules of Probability
- Thu May 16
LEC 13 Combinatorics
HW 6 Homework 6
Week 8 – Combinatorics, Independence
- Mon May 20
DISC 7 Groupwork 7
- Tue May 21
LEC 14 More Combinatorics
- Thu May 23
LEC 15 Independence and Bayes' Theorem
HW 7 Homework 7