Introduction to Machine Learning
A Deep Learning Approach
CSI 5325 | Spring 2021
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This course is an introduction to the major problems, techniques, and issues of learning from data. Emphasis is placed upon the topics of machine learning and problem solving. This is a field rapidly growing in which we create models for computers to learn how to make inferences, or make decisions, based on data all around us and even in its absence. The Python language will be used to illustrate various machine learning techniques.
- CSI 4336 - Introduction to Computation Theory
This course requires proficiency in the basic areas of computer science, but it also makes use of other important subjects in the area of mathematics: probability, statistics, calculus, linear algebra, optimization, etc. Please make sure you feel confident in all these subjects prior to starting this course.
You will be submitting data to Kaggle. You will need to create an account upon registration for the course.
Credit Hours: 3
Not sure if this course is right for you?
To find out, look around Kaggle Competitions and try to see if any of the featured competitions pose problems that challenge your intellect and your desire for knowledge on how computer can solve such problems. If you are intrigued, this course might be right for you.
It is the world's largest community of data scientists. They compete with each other to solve complex data science problems, and the top competitors are invited to work on the most interesting and sensitive business problems from some of the worlds biggest companies through competitions.
Kaggle provides cutting-edge data science results to companies of all sizes. They have a proven track-record of solving real-world problems across a diverse array of industries including life sciences, financial services, energy, information technology, and retail.
Dr. Rivas dreams that at least one team of students from Baylor will join a Kaggle competition and perform extremely well. He wants his students to represent Baylor proudly among other scientists and students. Dr. Rivas wants Baylor to be known as one of the best schools of computer science and machine learning in the U.S. Are you in?
[LFD] Y. S. Abu-Mostafa, et.al, Learning From Data.
The printed book contains most of the material required for this course, the rest of the material has been provided online by the authors here. You are responsible for having all the material available to you.
[DL4B] P. Rivas Deep Learning for Beginners.
This book contains introductory deep learning models and code in Python. You may find this useful for starting your project earlier.
The professor also recommends the following books for further reference:
- S. J. Russell, et.al. Artificial Intelligence: A Modern Approach. Pearson. 4rd Edition. 2020.
- D. Barber, et.al. Bayesian Reasoning and Machine Learning. Cambridge University Press. 2012.
Other resources include:
- The famous online course of Andrew Ng’s CS 229 at Stanford.
- Kaggle: tutorials, datasets, competitions. This is the pattform used to judge the most important machine learning competitions.
At the completion of this course, students will be able to:
- Discuss the theory of learning from data up to state of the art algorithms for general machine learning.[1,2]
- Demonstrate an understanding of the most important machine learning algorithms.[1,2]
- Develop ML applications through active, hands-on activities.[1,2,3]
- Compete to achieve the best machine learning solutions.[2,3,4]
- Make informed choices about further studies and careers in ML.[1,3]
- Humbly brag that they participated in a world class data science competition.[2,3,4]
Numbers in square brackets indicate the specific goals of the School of Engineering and Computer Science that are being fulfilled.
The weekly coverage might change as it depends on the progress of the class. However, you must keep up with the reading assignments.
|1||1/17 - 1/23||Introduction to Artificial Intelligence and Machine Learning.
Reading assignment: [LFD] Chapter 1.1. [DL4B] Chapter 1.
Homework 0 and Semester Project assigned.
Watch the welcome and first lecture here.
|2||1/24 - 1/30||The Science of Learning Algorithms.
Reading assignment: [LFD] Chapter 1.2., 1.3, and 1.4. [DL4B] Chapters 2 and 3.
Homework 1 assigned.
|3||1/31 - 2/6||Generalization Bounds and The VC Dimension.
Reading assignment: [LFD] Chapter 2.1.
|4||2/7 - 2/13||Achieving Generalization.
Reading assignment: [LFD] Chapter 2.2 and 2.3.
Homework 2 assigned.
Project proposal due.
Watch the ice lecture here.
|5||2/14 - 2/20||Linear Models for Classification and Regression.
Reading assignment: [LFD] Chapter 3.1 and 3.2. [DL4B] Chapters 4 and 5.
Homework 3 assigned.
Watch the refugee lecture here.
|6||2/21 - 2/27||Logistic Regression and The Z Space.
Reading assignment: [LFD] Chapter 3.3 and 3.4.
|7||2/28 - 3/6||The Problem of Overfitting.
Reading assignment: [LFD] Chapter 4.1.
Homework 4 assigned.
|3/5||Midterm Exam During Class.|
|8||3/7 - 3/13||Regularization and Model Validation.
Reading assignment: [LFD] Chapter 4.2. and 4.3
|9||3/14 - 3/20||Nearest Neighbor, Radial Basis, and k-means.
Reading assignment: [LFD] Chapter 6.1., 6.2, and 6.3.
|10||3/21 - 3/27||Traditional Neural Networks.
Reading assignment: [LFD] Chapter 7.1, 7.2, 7.3, 7.4, and 7.5. [DL4B] Chapter 6.
Homework 5 assigned.
|11||3/28 - 4/3||State-of-The-Art Neural Networks.
Reading assignment: [LFD] Chapter 7.6 and [DL4B] Chapters 7, 8, and 11.
Project milestone due.
|4/2 - 4/4||No class. Easter.|
|12||4/4 - 4/10||Support Vector Machines.
Reading assignment: [LFD] Chapter 8.
|13||4/11 - 4/17||Variational Autoencoders.|
Reading assignment: [DL4B] Chapter 9.
|14||4/18 - 4/24||Recurrent Neural Networks.
Reading assignment: [DL4B] Chapter 13.
Final project writeup due.
Student Project Presentations.
|15||4/25 - 5/1||Generative Adversarial Networks.
Reading assignment: [DL4B] Chapter 14.
Student Project Presentations.
|5/3||Final Exam at 4:30pm.|
Grades will be assigned based on the following breakdown:
Homework assignments: 40%
Final project: 20%
Midterm exam: 20%
Final exam: 20%
Letter Grade Distribution
Final letter grades will be assigned at the discretion of the
instructor, but here is a minimum guideline for letter grades:
A: 100-95, A-: <95-90,
B+: <90-87, B: <87-83, B-: <83-80,
C+: <80-77, C: <77-73, C-: <73-70,
D+: <70-65, D: <65-60,
- The class website contains the official course information (rivas.ai/csi5325s21). Please check it regularly for updates.
- All work in this course is strictly individual, unless the instructor explicitly states otherwise. While discussion of course material is encouraged, collaboration on assignments is not allowed. Collaboration includes (but is not limited to) discussing with anyone (other than the professor) anything that is specific to completing an assignment. You are encouraged to discuss the course material with the professor, preferably in office hours, and also by email.
- Bring any grading correction requests to your professor's attention within 2 weeks of receiving the grade or before the end of the semester, whichever comes first.
- Grades in the C range represent performance that meets expectations; Grades in the B range represent performance that is substantially better than the expectations; Grades in the A range represent work that is excellent.
- Grades will be maintained in the LMS course shell. Students are responsible for tracking their progress by referring to the online gradebook.
Attendance and Absences
- Attendance is expected and will be taken each class. You are allowed to miss 2 classes during the semester without penalty. Any further absences will result in point and/or grade deductions.
- A total of 8 absences will automatically cause an F grade.
- Students are responsible for all missed work, regardless of the reason for absence. It is also the absentee's responsibility to get all missing notes or materials.
In addition to skills and knowledge, Baylor University aims to teach students appropriate Ethical and Professional Standards of Conduct. The Academic Honesty Policy exists to inform students and Faculty of their obligations in upholding the highest standards of professional and ethical integrity. All student work is subject to the Academic Honesty Policy. Professional and Academic practice provides guidance about how to properly cite, reference, and attribute the intellectual property of others. Any attempt to deceive a faculty member or to help another student to do so will be considered a violation of this standard.
Instructor's Intended Purpose
The student's work must match the instructor's intended purpose for an assignment. While the instructor will establish the intent of an assignment, each student must clarify outstanding questions of that intent for a given assignment.
The student may not give or get any unauthorized or excessive assistance in the preparation of any work.
The student must clearly establish authorship of a work. Referenced work must be clearly documented, cited, and attributed, regardless of media or distribution. Even in the case of work licensed as public domain or Copyleft, (See: http://creativecommons.org/) the student must provide attribution of that work in order to uphold the standards of intent and authorship.
Online submission of, or placing one's name on an exam, assignment, or any course document is a statement of academic honor that the student has not received or given inappropriate assistance in completing it and that the student has complied with the Academic Honesty Policy in that work.
An instructor may impose a sanction on the student that varies depending upon the instructor's evaluation of the nature and gravity of the offense. Possible sanctions include but are not limited to, the following: (1) Require the student to redo the assignment; (2) Require the student to complete another assignment; (3) Assign a grade of zero to the assignment; (4) Assign a final grade of "F" for the course; and (5) Notify the Dean of the School of Computer Science and Mathematics about the issue. A student may appeal these decisions according to the Academic Grievance Procedure. (See the relevant section in the Student Handbook.) Multiple violations of this policy will result in a referral to the Conduct Review Board for possible additional sanctions.
Dr. Rivas takes academic honesty very seriously, after all, he also teach Ethics. Many studies, including one by Sheilah Maramark and Mindi Barth Maline have suggested that "some students cheat because of ignorance, uncertainty, or confusion regarding what behaviors constitute dishonesty" (Maramark and Maline, Issues in Education: Academic Dishonesty Among College Students, U.S. Department of Education, Office of Research, August 1993, page 5). In an effort to reduce misunderstandings, here is a minimal list of activities that will be considered cheating in this class:
- Using a source other than the optional course textbooks, the course website, or your professor to obtain credit for any assignment.
- Copying another student's work. Simply looking over someone else's source code is copying.
- Providing your work for another student to copy.
- Collaboration on any assignment, unless the work is explicitly given as collaborative work. Any discussion of an assignment or project is considered collaboration.
- Studying tests or using assignments from previous semesters.
- Providing someone with tests or assignments from previous semesters.
- Turning in someone else's work as your own work.
- Giving test questions to students in another class.
- Reviewing previous copies of the instructor's tests without permission from the instructor.
Data for Research Disclosure
Any and all results of in-class and out-of-class assignments and examinations are data sources for research and may be used in published research. All such use will always be anonymous.
About The Professor
Pablo Rivas is a senior member of the IEEE and ACM. His degrees are in computer science (B.S. '03), electrical engineering (M.S. '07), and electrical and computer engineering (Ph.D. '11 from the University of Texas at El Paso). Currently, He is an Assistant Professor of Computer Science at Baylor. Before that, he was at Marist College in New York.
At Marist College he had the opportunity to work on different aspects of machine learning, data science, big data, and large-scale pattern recognition. Perhaps you have heard on NPR about a project of Dr. Greg Hamerly where he was a collaborator on the detection of leukocoria (see leuko.net for more info), where they used deep learning and image-processing techniques, which he loves. Another recent research project originated after an internship at NASA Goddard Space Flight Center where he worked in the detection of a particular kind of atmospheric particle using different machine learning methods. He currently works to make that remote sensing project available on-line in real time.
In the past he worked in the industry as Software Engineer for about 8 years; thus, he is quite familiar with programming languages, particularly C++, Java, and Python, but he has used MATLAB in the past.
He has been recognized for his creativity and academic excellence; for instance, he received the IEEE Student Enterprise Award in 2007, and the Research Excellence Award from the Paul L. Foster Health Sciences School of Medicine of Texas Tech University in 2009. In 2011, he had the honor of being inducted to the International Honor Society Eta Kappa Nu (HKN).
When he is not having fun doing research or teaching, he also likes to play basketball, code, eat pizza with friends, or go to the movie theater with his beautiful wife Nancy. The pandemic has affected some of these activities.
How to Contact The Professor
Dr. Rivas' office number is 304 @ Cashion, and office hours are:
- Wednesdays 4:30-5:30 PM
- Fridays 4:30-5:30 PM
Office hours are online and you can join here.
He is glad to talk to students during and outside of office hours. If you can't attend office hours, please make an appointment for another time, or just stop by if you see the door open (unlikely during the pandemic). If you are going to stop by it is a good idea to check his schedule and call first to make sure he is not busy; the number is (254) 710-3385.
If extra help is needed, there are private tutors available at the student's expense at Baylor's Office of Academic Support Programs.
Note: Any student who needs learning accommodations should inform Dr. Rivas immediately at the beginning of the semester. The student is responsible for obtaining appropriate documentation and information regarding needed accommodations from the Office of Access and Learning Accommodation (OALA), available online here, and providing it to the professor early in the semester.
Some content taken from a syllabus by Greg Hamerly and used with permission.
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