
DR. MUGIZI ROBERT RWEBANGIRA INSTRUCTOR 



Ade 
David 
Kristian 



Rhonda 
Robert 
Seun 



Shawna 
Sulaimon 
Tonique 
Date 
Day 
Lec # 
Topic 
Materials 
1/9 
M 
1 
What is Machine
Learning? Probability Review. Bayes Rule 

1/11 
W 
2 
Introduction to
Octave/Matlab(variables, colon notation) 

1/16 
M 
MARTIN LUTHER KING JR DAY 

1/18 
W 
3 
More on Octave
(matrix operations) 

1/23 
M 
4 
Simple Linear
Regression 

1/25 
W 
5 
Linear Regression
(one variable) 

1/30 
M 
6 
Linear Regression
(one variable) 

2/1 
W 
7 
Linear Regression
(multiple variables, polynomial regression) 

2/6 
M 
8 
Naïve Bayes
Classifier 

2/8 
W 
9 
Naïve Bayes
Classifier (continued) 

2/13 
M 
10 
Perceptron 

2/15 
W 
11 
Perceptron
(continued) 

2/20 
M 
PRESIDENT’S DAY 


2/22 
W 
!!! TEST 1!!! 

2/27 
M 
12 
Perceptron
(continued) 

2/29 
W 
13 
Gradient Descent 

3/5 
M 
14 
Gradient Descent
(continued) 

3/7 
W 
MIDTERM 

3/12 
M 
SPRING BREAK 


3/14 
W 
SPRING BREAK 


3/19 
M 
15 
Artificial Neural
Networks 

3/21 
W 
16 
Artificial Neural
Networks (continued) 

3/26 
M 
!!CLASS CANCELLED!! 

3/28 
W 
!!CLASS CANCELLED!! 

4/2 
M 
17 
Project
Presentations 

4/4 
W 
!!CLASS CANCELLED!! 


4/9 
M 
18 
Project
Presentations 

4/11 
W 
19 
Project
Presentations 

4/16 
M 
20 
Project
Presentations 

4/18 
W 
21 
Project
Presentations 