# Schedule
| Week |
Date |
Topics |
E |
M |
P |
| W1 |
02-Jun |
Perceptron Algorithm |
E1 |
M1 |
P1 |
|
03-Jun |
Logistic Regression |
E2 |
M2 |
|
|
04-Jun |
Neural Network |
E3 |
M3 |
|
|
05-Jun |
Backpropagation |
E4 |
|
|
| W2 |
09-Jun |
Support Vector Machine |
E5 |
M4 |
P2 |
|
10-Jun |
K-Nearest-Neighbors, Decision Tree |
E6 |
|
|
|
11-Jun |
Computer Vision |
E7 |
M5 |
|
|
12-Jun |
Deep Learning, Convolutional Network |
E8 |
|
|
| W3 |
16-Jun |
Natural Language and Speech |
E9 |
M6 |
P3 |
|
17-Jun |
Naive Bayes, Bayesian Network |
E10 |
|
|
|
18-Jun |
Hidden Markov Model |
E11 |
M7 |
|
|
19-Jun |
Recurrent Neural Network |
E12 |
|
|
| W4 |
23-Jun |
Markov Decision Process |
E13 |
|
|
|
24-Jun |
Reinforcement Learning |
E14 |
|
|
|
25-Jun |
Midterm Review, Part I |
|
|
P6 |
|
26-Jun |
Midterm Review, Part II |
|
|
|
| W5 |
30-Jun |
Midterm Exam, Part I |
|
|
|
|
01-Jul |
Midterm Exam, Part II |
|
|
|
|
02-Jul |
Hierarchical Clustering, K-Means Clustering |
E15 |
M8 |
P4 |
|
03-Jul |
Principal Component Analysis |
E16 |
|
|
| W6 |
07-Jun |
Uninformed Search, Robotics |
E17 |
M9 |
P5 |
|
08-Jul |
Informed Search |
E18 |
|
|
|
09-Jul |
Hill-Climbing, Simulated Annealing |
E19 |
M10 |
|
|
10-Jul |
Genetic Algorithms, Constraint Satisfaction |
E20 |
|
|
| W7 |
14-Jul |
Game Theory |
E21 |
M11 |
|
|
15-Jul |
Minimax Game, Alpha-Beta Pruning |
E22 |
M12 |
|
|
16-Jul |
Repeated Games |
E23 |
|
|
|
17-Jul |
Mechanism Design |
E24 |
|
|
| W8 |
21-Jul |
Final Review, Part I |
|
|
P6 |
|
22-Jul |
Final Review, Part II |
|
|
|
|
23-Jul |
Final Exam, Part I |
|
|
|
|
24-Jul |
Final Exam, Part II |
|
|
|
📗 Click the W1, W2, etc to see the lecture slides and the links to the lecture examples (E), math homework (M), and programming homework (P) of the week.
📗 Lecture recordings will be posted on YouTube, and the links will be posted on this website. Official lecture times will be used for brief reviews, going over examples, and quizzes, on Canvas BBCollaborateU, not recorded.
📗 The topics are subject to change.
📗 The optional textbooks are (RN) Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
Link and (SS) Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Schwartz and Shai Ben-David
Link.
📗 Any edition of RN is okay. SS is freely available online. No homework problem will be assigned from the textbooks. For students planning to take CS760 and CS761, SS is highly recommended.
# Grading Scheme
| Component |
Frequency |
Number |
Max Points Each |
| Quizzes |
Daily |
20 |
1 |
| Math (Written) |
Weekly |
10 |
2 |
| Programming |
Weekly |
5 |
8 |
| Midterm |
Once |
1 |
10 |
| Final |
Once |
1 |
10 |
📗 The recommended programming language is Java and Python. Code written in other languages will be accepted. The course staff will only be able to provide help with code in Java and Python.
📗 The lowest 4 quiz and 2 math homework grades out of 24 or 12 are dropped. If you cannot attend any of the quizzes due to scheduling conflicts, your midterm and final exam grades will count for 20 points each. If you cannot attend some of the quizzes, you can use the extra math (two of them) and programming homework (one of them) grades to replace those too.
📗 The lowest programming homework grade can be replaced by a programming project you choose (P6): the project must implement at least one of the algorithms covered during the lecture on a dataset of your choice.
| Grade |
Letter |
Numeric |
| 90+ |
A |
4 |
| 85+ |
AB |
3.5 |
| 80+ |
B |
3 |
| 75+ |
BC |
2.5 |
| 70+ |
C |
2 |
| 60+ |
D |
1 |
| 0+ |
F |
0 |
📗 The conversion table is subject to minor modification.
📗 Midterm and final exam grades will be curved by dropping the questions with a negative point biserial correlation coefficient (RPBI < 0) or less than a quarter of the students answered correctly (PROB < 25%). The students who answered those correctly keep the points as bonus points. Quiz and homework grades will not be curved. The final grade will not be curved.
| Exams |
Time |
Format |
Coverage |
| Midterm |
2 hours |
20 Short Answer |
W1 to W3 |
| Final |
2 hours |
20 Short Answer |
W5 to W7 |
# Admin
📗 TA: Ainur Ainabekova
📗 Office Hours: Tuesdays 5:00 to 6:00 for Programming Homework related questions
📗 TA: Dandi Chen
📗 Office Hours: Thursdays 5:00 to 6:00 for Math Homework related questions
📗 Instructor: Young Wu
📗 Office Hours: Tuesdays, Wednesdays, Thursdays, Fridays after lecture.
# Course Website
📗 This webpage (for lecture notes and assignments).
📗 Summer 2019 Course:
2019.
📗 Canvas (for grades):
Link.
📗 Piazza (for discussion):
Link.
📗 Socrative (for quizzes):
Link. The room numbers are "CS540C" for graded quizzes and "CS540" for anonymous feedback: use your wisc ID to log in. You can also use the following room links:
CS540C or
CS540.
📗 Professor Jerry Zhu:
2020.
📗 Professor Yingyu Liang:
2018 and
2019
📗 Professor Charles Dyer:
2019;
📗 Professor Jude Shavlik:
2016.
Last Updated: November 09, 2021 at 12:29 AM