Lectures

Lecture Schedule

Week	Day	Date	Topic	Due
1	R	26-Aug	Signal Processing Review (PDF)
2	T	31-Aug	Linear Prediction (PDF)
	R	2-Sep	LPC-10 Speech Coding (PDF)
3	T	7-Sep	Linear Algebra Review (PDF; onenote; ipynb; html)	MP1
	R	9-Sep	Multidimensional Signal Processing (PDF; onenote; ipynb; html)
4	T	14-Sep	Optical Flow (PDF; novideo)
	R	16-Sep	Image Interpolation (ipynb; HTML)	HW2
5	T	21-Sep	Multivariate Gaussians (PPTX; PDF; onenote)	MP2
	R	23-Sep	Exam 1 Review (PDF)
6	T	28-Sep	Exam 1	Exam 1
	R	30-Sep	Gaussian and Mixture Classifiers (PDF)
7	T	5-Oct	PCA (PDF)	HW3
	R	7-Oct	Expectation Maximization (PDF)
8	T	12-Oct	Hidden Markov Models (PDF; onenote)	MP3
	R	14-Oct	Baum-Welch Algorithm (PDF; onenote)
9	T	19-Oct	Numerical Issues (PDF)	HW4
	R	21-Oct	Neural Nets (PDF)
10	T	26-Oct	Backpropagation (PDF)	MP4
	R	28-Oct	Exam 2 Review (PDF; Problem 3 solution)
11	T	2-Nov	Exam 2	Exam 2
	R	4-Nov	Convolutional Neural Networks (PDF; forward.gif; backward.gif; onenote )
12	T	9-Nov	Faster RCNN (PDF )	HW5
	R	11-Nov	Partial and Total Derivatives (PDF; onenote)
13	T	16-Nov	RNN (PDF; onenote)	MP5 due Wednesday
	R	18-Nov	LSTM (PDF)
14	T	30-Nov	Pytorch Intro & MP6 Walkthrough	HW6
	R	2-Dec	Final Review (PDF; onenote1; onenote2 )
15	T	7-Dec	Final Review (note1; note2; note3; note4; note5)	MP6 due Wednesday
		13-Dec	Final Exam	Final