Introduction to Machine Learning, CS1675
Spring 2019, University of Pittsburgh / TA Website
Spring 2019, University of Pittsburgh / TA Website
Recitation Schedule
Week 1
No recitations
Week 2
Matlab Tutorial
Material: [Slides]
Material: [Slides]
Week 3
Math and Probability for ML Recap
Material: [Slides]
Material: [Slides]
Week 4
No recitation (University recess for extreme weather)
Week 5
Review on homework 1 and 2
Material: [Slides]
Material: [Slides]
Week 6
Review on homework 3
(Derive ML estimate for exponential dist.) Material: [Slides]
(Derive ML estimate for exponential dist.) Material: [Slides]
Week 7
Review on homework 4
(Online Gradient Descent) Material: [Slides]
(Online Gradient Descent) Material: [Slides]
Week 8
Review on homework 5
(Naive Bayes and ROC curve) Material: [Slides]
(Naive Bayes and ROC curve) Material: [Slides]
Week 9
Review for midterm
Material: [Slides]
Material: [Slides]
Week 10
Bayes Theorem Exercise Example
Material: [Slides]
Material: [Slides]
Week 11 - 13
Homework Solutions : slides are only available directly with TA.
Week 14
Reinforcement Learning
Material: [Slides]
Material: [Slides]
Information
- Syllabus and Course Information
please refer CS1675 website http://people.cs.pitt.edu/~milos/courses/cs1675/
TA: Jeongmin Lee
- Office 5324 Sennett Square
- Email
Office Hours
- Tuesday: 2:00pm-5:00pm
- Wednesday: 4:00pm-5:00pm
- Thursday: TBD