CS 438/638 Applied Machine Learning
Course Syllabus
Spring 2024

COURSE GOALS

1. To learn practical side of machine learning for applications
2. To learn the process of applying machine learning to a variety of problems
3. To learn unsupervised learning
4. To learn supervised learning

METHODOLOGY

Lecture and interactive problem solving.

Recommended Reference Books:
• Artificial Intelligence: A Modern Approach
• Introduction to Data Mining
• Coursera, Deep Learning Specification

APPRAISAL

Participation: 10% of the total
Individual Assignments (graduate students will have one more assignment than undergraduate students): 30% of the total
Term Project: 30% of the total
Examination (1 exam): 30% of the total

GRADING

91+ = A; 89+ = A-;
87+ = B+; 83+ = B; 80+ = B-;
77+ = C+; 73+ = C; 70+ = C-;
67+ = D+; 63+ = D; 60+ = D-;
0+ = F;

READING

We will read from the recommended text book, various sources on the web, and slides that will be made available on the web site. The schedule for the readings are given on the schedule web page.

OTHER POLICIES

1. Homework:
   • Homework is due exactly at the prescribed time. No late homework is accepted.
   • Submit the softcopy of the assignment through your UMass Online Blackboard Blackboard Learning System (BLS) account at https://umb.umassonline.net/.
   • Any questions or complaints regarding the grading of an assignment or test must be raised within one week after the score or the graded assignment is made available (not when you pick it up).
2. Providing answers for any examination when not specifically authorized by the instructor to do so, or, informing any person or persons of the contents of any examination prior to the time the examination is given is considered cheating.
3. Penalty for cheating will be extremely severe. Use your best judgment. If you are not sure about certain activities, consult the instructor. Standard academic honesty procedure will be followed for cheating and active cheating automatically results F in the final grade. Please check University Policy on Academic Standards and Cheating for additional information.
4. You are expected to come fully prepared to every class.
5. No incomplete grade under nearly all situations.
6. There is no formal attendance policy. However, you are responsible for everything discussed in class. You may receive a zero for lack of participation.
7. Pay very careful attention to your email correspondence. It reflects on your communication skills. Avoid using non-standard English such as "how r u?" in your email message. In addition, I recommend you put the class number 438/697 and a brief summary of your question in your email subject. For example,

   Subject: CS 438/638 A question on supervised learning

8. I immediately discard anonymous emails.
9. The ringing, beeping, buzzing of cell phones, watches, and/or pagers during class time is extremely rude and disruptive to your fellow students and to the class flow. Please turn off all cell phones, watches, and pagers prior to the start of class.

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