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College of Professional and Global Education · School of Information

Information Technology Tools and Applications - Advanced - Math for Machine Learning
INFO 246

  • Spring 2023
  • Section 15
  • 1 Unit(s)
  • 01/25/2023 to 02/28/2023
  • Modified 05/22/2023

Canvas Information: Courses will be available January 25th, 6 am PT.

You will be enrolled in the Canvas site automatically.

Contact Information

Dr. Souvick Ghosh
Other contact information: Virtual/Online
Office Hours: Virtually (by appointment) via telephone or online

Course Information

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.

Course Description and Requisites

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.


INFO 202, other prerequisites may be added depending on content.

Classroom Protocols


Students are expected to participate fully in all class activities. It is expected that students will be open-minded and participate fully in discussions in class and debate in a mature and respectful manner. Use of derogatory, condescending, or offensive language including profanity is prohibited. Disagreement is healthy and perfectly acceptable. Expressing disagreement should always include an explanation of your reasoning and, whenever possible, evidence to support your position. In accordance with San José State University's Policies, the Student Code of Conduct, and applicable state and federal laws, discrimination based on gender, gender identity, gender expression, race, nationality, ethnicity, religion, sexual orientation, or disability is prohibited in any form.

Program Information

Course Workload

Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five 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 seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted 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 Goals

Core Competencies (Program Learning Outcomes) Supported

INFO 246 supports the following core competencies:

  1. - Core Competencies for this course and/or topic are being updated at this time.

Course Learning Outcomes (CLOs)

Upon successful completion of the course, students will be able to:

  1. CLOs for this topic have not yet been defined.

Course Materials


Recommended Textbooks:

  • Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press. Available as Free eBook.
  • Thomas, G. (2018). Mathematics for machine earning. University of California, Berkeley. Available as Free PDF.

Course Requirements and Assignments

Summary of assignments and points earned:

  • Blog Entries/Discussion Forums - 50 points [CLOs 1-4]
    • Introduction - 10 point
    • 2 blog entries, 20 points each = 40 points
  • Assignments – 2 assignments, 25 possible points for each assignment = 50 points [CLOs 1-4]

The total number of points for this class is 100.

  1. Lesson Dates: Most Lesson and Worktime periods begin on a Wednesday. Lesson materials are posted at least a week in advance.
  2. Due date Times are 11:59 pm Pacific time zone.
  3. 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 follow-up 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.

Grading Information

The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:

97 to 100A
94 to 96A minus
91 to 93B plus
88 to 90B
85 to 87B minus
82 to 84C plus
79 to 81C
76 to 78C minus
73 to 75D plus
70 to 72D
67 to 69D minus
Below 67F


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 BS-ISDA);
    For core courses in the MLIS program (not MARA, Informatics, BS-ISDA) — 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 S16-9 (PDF), relevant university policy concerning all courses, such as student responsibilities, academic integrity, accommodations, dropping and adding, consent for recording of class, etc. and available student services (e.g. learning assistance, counseling, and other resources) are listed on the Syllabus Information web page. Make sure to visit this page to review and be aware of these university policies and resources.

Course Schedule

Course Modules

A detailed course calendar is available from the course site on the first day of the semester.



Dates (1)

Lesson topic & readings

Assignments / Activities (3)(4)

Due-Date (2)



Jan 25-31

Pre-course Assessment


Optional Resources


1: Introduction to the Course

Introduction to the Course

Bias in ML and AI – a brief perspective


Limits and continuity

Derivatives: definition and basic rules

Derivatives: chain rule

Applications of derivatives

·   Analyzing functions

Pre-course Test: Take the online Calculus readiness test (UCSD offers the free test here: and submit your score report. (Ungraded)




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






Jan 31







Jan 29 & 31













5 & 5







Feb 1-7

2: Linear Algebra

Linear Algebra

Linear Equations

·       Matrices

·       Determinant

·       Eigenvalues and vectors

Assignment 1: Solving linear equations using practical examples

Feb 7



Feb 8-14

3: Vector Calculus

Vector Calculus

·       Partial Differentiation

·       Gradients

·       Backpropagation

Blog 2: Reading Summary - First post & two responses

Feb 12 & 14

10 & 10


Feb 15-21

4: Probability


·       Discrete and Continuous Probabilities

·       Sum and Product Rule

·       Bayes Theorem

·       Gaussian Distribution

Blog 3: Reading Summary - First post & two responses

Feb 19 & 21

10 & 10


Feb 22-28

5: Developing ML Models

Developing ML Model from Data

·       Learning

·       Parameter Estimation

·       Model Selection

·       Dimensionality Reduction

Assignment 2: Exploring Bayes’ Theorem for ML application

Feb 28


  1. Lesson Dates: Most Lesson and Worktime periods begin on a Wednesday. Lesson materials are posted at least a week in advance.
  2. Due-date Times are 11:59 pm Pacific time zone.
  3. Blogs: Initial posts need to start early, and responses are also required. See details in the Discussion Instructions on the course site.
  4. Late Policy: Late assignments will have a 10%-point reduction for each day up to 5 days and will not be accepted after 5 days.