📗 -> Probability & Statistical Modeling for Computer Science
short mode
not done
tags include #class
description includes PROB🔗 Critical Links
🔶 Course Description
Univariate and multivariate distributions. Estimation and model building. Markov/Hidden Markov models. Applications to data mining, networks, security, software engineering and bioinformatics.
❗ Important
Instructor:
- Course Instructor Name - Email
TA:
- TA Name - Email
- Course Instructor Name - Email
ECS132-Cheatsheet
ECS132-Final-Prep
ECS132-Extra-Credit
📄 Class Material
Week 1 - Basic Probability
Week 2 - Basic Probability
Week 3 - Discrete Random Variables
-
Laptop low battery, lecture in notebook. Intro to RVs, EV and variance
-
Midterm 1
Week 4: Continuous Variables
Week 5:
Skipped discussion
Week 6:
Discussion was just content review on:
- Discrete RVs, Continuous RVs, Exponential, Gaussian, Standardization, Z-Scores
- ECS132-L10
- ECS132-L11
Week 7:
- ECS132-L12
- ECS132-11-12-24
Midterm 2
- ECS132-11-12-24
Week 8:
Week 9:
Thanksgiving
Week 10:
- ECS132-L16: COURSE OVERVIEW