INFO 24617 (1Unit)
Information Technology Tools and Applications – Advanced Topic: Math for Machine Learning
Spring 2022 Syllabus
Dr. Souvick Ghosh
Email
Other contact information: Virtual/Online
Office Hours: Virtually (by appointment) via telephone or online
Syllabus Sections Prerequisites CLOs Competencies Textbooks 
iSchool Resources Canvas Login and Tutorials eBookstore 
Canvas Information: This 1unit course will be available beginning Wednesday, January 26th. Students must log on to the Canvas site by the second day of the semester and begin coursework.
This course runs from January 26th  March 1st.
You will be enrolled in the Canvas site automatically.
Course Description
This course offers a practical introduction to the various mathematical theories necessary for machine learning and data science. It provides fundamental knowledge in several mathematical concepts, in a practical way, suitable for beginners and those without a math background.
Course Requirements
General Requirements
Students are expected to check the course site several times each week. Assignments must be submitted by 11:59 pm Pacific Time on the due date. Contact the instructor prior to the due date in the case of serious illness or emergency.
Lectures, discussions, assignments, and rubrics will be posted to the Canvas course management system. Links to additional materials will be provided in Canvas as well.
Summary of assignments and points earned:
 Blog Entries/Discussion Forums  50 points [CLOs 14]
 Introduction  10 point
 2 blog entries, 20 points each = 40 points

Assignments – 2 assignments, 25 possible points for each assignment = 50 points [CLOs 14]
The total number of points for this class is 100.
Course Modules
A detailed course calendar is available from the course site on the first day of the semester.
Week 
Dates (1) 
Lesson Topic & Readings (3) 
Assignment/Activities (4) 
Due Date (2) 
Points 
1 (short week) 
Jan 2630 
Selfassessment Optional Resources 
Precourse Test: Take the online Calculus readiness test (UCSD offers the free test here: https://mdtpwri.ucsd.edu/practice_tests/index.php?show_instructions=3) and submit your score report. (Ungraded)

Jan 30 
0 
2 
Jan 31Feb 6 
Introduction to the Course Bias in ML and AI – a brief perspective Calculus

Blog 1: Introduce yourself to the class and discuss your learning objectives from this course. Share an example situation where you expect the skills learned in this class to help you.  First post & two responses

Feb 4 & 6 
5 & 5 
2 
Feb 713 
Linear Algebra

Assignment 1: Solving linear equations using practical examples 
Feb 13 
25 
3 
Feb 1420 
Vector Calculus

Blog 2: Reading Summary  First post & two responses 
Feb 18 & 20 
10 & 10 
4 
Feb 2127 
Probability

Blog 3: Reading Summary  First post & two responses 
Feb 25 & 27 
10 & 10 
5 
Feb 28Mar 6 
Developing ML Model from Data

Assignment 2: Exploring Bayes’ Theorem for ML application 
Mar 6 
25 
 Lesson Dates: Most Lesson and Worktime periods begin on a Wednesday. Lesson materials are posted at least a week in advance.
 Duedate Times are 11:59 pm Pacific time zone.
 Introduction and Two Blog Entries/Forum Discussions: Initial posts need to start early, and responses are also required. See details in the Discussion Instructions on the course site.*NOTE: For weeks with required discussion board postings, students should provide their initial post by Friday at midnight (Pacific Time), to leave ample time for followup discussion. Please participate actively in the required discussions.
Late Policy
 Late assignments will not be accepted after 5 days past the due date. Late assignments submitted after the assignment deadline will receive a 10% point reduction for each day up to 5 days based on the total point value of the assignment. For example, a 25 point assignment would have a daily 2.5 point reduction; a 15 point assignment would have a daily 1.5 point reduction; a 5 point assignment would have a daily 0.5 point reduction. No points will be awarded after 5 days late.
 Discussion board postings will not be accepted for credit after the week's discussion has ended.
 All course materials must be completed by the last day of the class.
Course Workload Expectations
Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of fortyfive hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.
Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.
Student time outside of class:
In any sevenday period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computerassisted instruction; building websites, blogs, databases, social media presentations; attending a study group;contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.
Course Prerequisites
INFO 202, other prerequisites may be added depending on content.
Course Learning Outcomes
Upon successful completion of the course, students will be able to:
 CLOs for this topic have not yet been defined.
Core Competencies (Program Learning Outcomes)
INFO 246 supports the following core competencies:
  Core Competencies for this course and/or topic are being updated at this time.
Textbooks
Recommended Textbooks:
 Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press. Available as Free eBook.
Grading Scale
The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:
97 to 100  A 
94 to 96  A minus 
91 to 93  B plus 
88 to 90  B 
85 to 87  B minus 
82 to 84  C plus 
79 to 81  C 
76 to 78  C minus 
73 to 75  D plus 
70 to 72  D 
67 to 69  D minus 
Below 67  F 
In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:
 C represents Adequate work; a grade of "C" counts for credit for the course;
 B represents Good work; a grade of "B" clearly meets the standards for graduate level work or undergraduate (for BSISDA);
For core courses in the MLIS program (not MARA, Informatics, BSISDA) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B or lower) after the first attempt you will be placed on administrative probation. You must repeat the class if you wish to stay in the program. If  on the second attempt  you do not pass the class with a grade of B or better (not B but B) you will be disqualified.  A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.
Graduate Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA). Undergraduates must maintain a 2.0 Grade Point Average (GPA).
University Policies
Per University Policy S169, universitywide policy information relevant to all courses, such as academic integrity, accommodations, etc. will be available on Office of Graduate and Undergraduate Programs' Syllabus Information web page at: https://www.sjsu.edu/curriculum/courses/syllabusinfo.php. Make sure to visit this page, review and be familiar with these university policies and resources.
In order to request an accommodation in a class please contact the Accessible Education Center and register via the MyAEC portal.
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