Syllabus of CSCE883 Machine Learing

Course Summary

A comprehensive introduction to main algorithms of Machine Learning and their applications

Course Objective At the end of the class, students are expected to be able to

  1. To gain understanding of principles of machine learning techniques
  2. To solve real-world problems using ML techniques
  3. To Benchmark and evaluating ML techniques

Prerequisite You are expected to have some basic programming skills using C, or C++ or java.


Introduction to Machine Learning by Ethem Alpaydin ISBN-10: 0262028182 ISBN-13: 978-0262028189 2014 The MIT Press

(you can find cheap books at

Meeting Time(s): Meeting Time: TTH 1:15PM- 2:30PM
Classroom: 2A05 Swearinger Engineering Center
Instructor: Dr. Jianjun Hu
Email: jianjunh AT
Office: 2223 Storey Innovation Center
Office Hours: TTH 3:00PM-4:00PM or by Appointment/stop buy.

Lecture Notes/Assignments/Readings

Lecture notes, homework assignments will be available at the class website. You will be responsible for downloading them to prepare for class and homework.

Supplementary Readings Extensive reading materials will be provided each week to develop a broad understanding of algorithms


Your course grade will be based on homework assignments, 1 mid-term exam, 1 final project, and attendance. The weights given to these components is:

  1. Homework assignments (40%)
  2. In-class midterm exams (1) (20 %)
  3. Final project(35%)
  4. Attendance and participation(5%)

Grade: A (90-100%), B+ (85-90%), B (80-85%), C+ (75-80%), C (70-75%), D+ (65-70%), D (60-65%), and F (0-60%)

Covered Topics: (These are tentative topics. Changes may be made based on the available time.)

Many interesting topics will be covered: 1 Introduction
2 Supervised Learning 17
3 Bayesian Decision Theory 39
4 Parametric Methods 61
5 Multivariate Methods 85
6 Dimensionality Reduction 105
7 Clustering 133
8 Nonparametric Methods 153
9 Decision Trees 173
10 Linear Discrimination 197
11 neural network & deep learning 229
12 Local Models 275
13 Hidden Markov Models 305
14 Assessing and Comparing Classification Algorithms 327
15 Combining Multiple Learners 351
16 Reinforcement Learning 373

A due time will accompany each homework assignment. Late homework is not accepted without prior approval from the instructor. Homework may have different weight when it is counted into your final grade. The due time of the homework will be at the beginning of the class. Some homework questions need programming. You need to turn in your code to the departmental electronic dropbox. Code should be written in C or C++ and should be tested in the departmental Linux computers. Class attendance is required as claimed in University policy and a student is responsible for all the material covered in the class. Not knowing changes to class policy/homework/etc. is NOT an acceptable reason for non-compliance. Both midterm and final exams are closed to books and notes, except for a single-side letter-size cheat sheet for each midterm and a double-side one for the final exam. Grades of homework and exams will be uploaded into Blackboard when they are available.

Academic Integrity: Homework and examinations are expected to be the sole effort of the student submitting the work. Students are expected to follow the Code of Student Academic Responsibility. Every instance of a suspected violation will be reported. Students found guilty of violations of the Code will receive the grade of F for the course in addition to whatever disciplinary sanctions are applied.