INFO 287-15
Seminar in Information Science
Topic: Problem Solving with Data Part II (2-Units)
Spring 2022 Syllabus

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

Syllabus Links
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Canvas Information: This 2-unit course will be available on Canvas beginning March 14 at 6 am PT. This course runs from Mar 14th - May 16th. 

You will be enrolled in the Canvas site automatically.

Course Description

This course offers an advanced understanding of data science through the application of machine and deep learning techniques to real-life data problems. The course covers the development of tool-based and coding-based solutions to analyze and visualize data. It will provide advanced knowledge in multiple machine learning tools and platforms, data processing techniques, and evaluation.

Course Requirements


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 - 13 points [CLOs 1,2,3,6]
    • Introduction - 2 point
    • 3 blog entries, 5 points each = 15 points
  • Assignments - Best 4 out of 5 assignments, 12 points each = 48 points [CLOs 1,2,3,5]
  • Final Project - 35 points  [CLOs 1,2,3,4,5]
    • Project Groups and Idea - 5 points
    • Project Presentation - 10 points
    • Project Report - 15 points
    • Peer Evaluation - 5 points

The total number of points for this class is 100.

Assignments Due

Students are expected to check the course site several times each week. Unless otherwise noted, each module begins on Wednesday and ends on Tuesday. Assignments will be due by midnight (Pacific Time) on the due date. Contact the instructor prior to the due date in the case of serious illness or emergency.

Course Calendar

A detailed Course Calendar will be available in Canvas on the first day of the semester. The table below provides a summary of course topics and assignment due dates. It is subject to minor changes, that will be announced with fair notice.



Lesson topic & readings(3)

Assignments / Activities(4)





Mar 14-20


1: Introduction to INFO 287-15

Lecture with recordings + selected readings.

  • Data Mining
  • Data Science
  • Machine Learning


Blog 1 Posts: Why should we study machine learning? Reflect on how it will help you in your workplace/profession.

-First post and two responses

Mar 18 & 20



March 18 & 20

1 & 1




3 & 2


Mar 21-27

2: Ethics of Machine Learning

Lecture with recordings + selected readings.

  • Fairness, Accountability, Transparency, and Explainability in ML
  • Steps to build fair AI
  • How to deal with unbalanced datasets

Project Groups and Idea

-Identify an interesting topic

-Develop Research Questions on the topic

Blog 2 Posts: Why transparency and fairness are important in machine learning? Reflect on application scenarios.

-First post and two responses

March 27







March 25 & 27








3 & 2

March 28 - April 1:Spring Break and Cesar Chavez Day

3 & 4

Mar 28-Apr 10

3: Clustering

Lecture with recordings + selected readings.

Assignment 1

Apr 3



April 11-17

4: Classification

Lecture with recordings + selected readings.

Assignment 2

Blog 3 Posts: Real-world situations to differentiate between supervised and unsupervised learning scenarios.

-First post and two responses

Apr 10




Feb 20 & 23





3 & 2


April 18-24

5: Predictive Analysis

Lecture with recordings + selected readings.

Assignment 3

April 24



Apr 25-May 1

6: Working with Text

Lecture with recordings + selected readings.

9.1: Introduction to Deep Learning

Assignment 4

May 1



May 2-8

7: Analyzing YouTube Data

Lecture with recordings + selected readings.

  • Using Google API to collect data
  • Using ML to analyze data

9.2: Application of Deep Learning 1

Assignment 5

Group Project Presentation

May 8

May 8




May 9-16

8: Analyzing Yelp Data

Lecture with recordings + selected readings.

  • Using Google API to collect data
  • Using ML to analyze data

9.3: Application of Deep Learning 2

Course Wrap Up

Peer Evaluation

Group Project Report

May 10

May 16



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

Course Workload Expectations

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 Prerequisites

INFO 200, students will be expected to know HTML/CSS (as taught in INFO 240) or obtained via work experience

Course Learning Outcomes

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

  1. Understand the theory behind various machine learning algorithms and their application to real-world data problems.
  2. Collect real-world data using APIs, or from databases, then clean and process the data to make it useful for analysis.
  3. Analyze and compare machine learning tools and algorithms and apply them suitably for solving problems.
  4. Build simple deep neural models for prediction tasks.
  5. Create visualizations to explore and analyze data.
  6. Understand the importance of transparency and fairness in machine learning.

Core Competencies (Program Learning Outcomes)

INFO 287 supports the following core competencies:

  1. G Demonstrate understanding of basic principles and standards involved in organizing information such as classification and controlled vocabulary systems, cataloging systems, metadata schemas or other systems for making information accessible to a particular clientele.
  2. H Demonstrate proficiency in identifying, using, and evaluating current and emerging information and communication technologies.


Recommended Textbooks:

  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. Available free Onlinearrow gif indicating link outside sjsu domain
  • Jamsa, K. (2020). Introduction to data mining and analytics. Jones & Bartlett Learning. Available through Amazon: 1284180905arrow gif indicating link outside sjsu domain
  • Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. MIT press. Available Free Onlinearrow gif indicating link outside sjsu domain
  • Severance, C. (2016). Python for everyone: Exploring data in Python 3. Create Space Independent Publishing Platform. Available through Amazon: 1530051126arrow gif indicating link outside sjsu domain
  • Shah, C. (2020). A hands-on introduction to data science. Cambridge University Press. Available through Amazon: 1108472443arrow gif indicating link outside sjsu domain
  • Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge University Press. Available Free Onlinearrow gif indicating link outside sjsu domain
  • Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2020). Dive into deep learning. Unpublished Draft. Available free Onlinearrow gif indicating link outside sjsu domain

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 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, university-wide 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: 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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