CSE 421: Machine Learning

Offered Under: Mechatronics & Robotics
Description

Introduction to Machine Learning; Classification of learning: unsupervised and supervised learning, connectionist learning, reinforcement learning, machine discovery; Supervised learning: Information theoretic decision tree learner, best current hypothesis search, candidate elimination (version space) algorithm, learning in the first order Horn clause representation, inductive logic programming, applications; Unsupervised learning: hierarchical clustering, category utility, incremental and non-incremental algorithms for hierarchical clustering, applications; Connectionist learning: introduction to neural networks, Feed forward and recurrent networks, perception, multilayer feed forward networks, backpropagation algorithm for training a feed forward network, applications; Genetic algorithms: genetic operators, fitness function, genetic algorithm in supervised learning framework, applications.


Prerequisites:
  • None

Course Type Minor
Credit Hour 3
Lecture Hour 45
Expected Outcome(s):
  • Be able to articulate key concepts and principles in Machine learning.
  • Be able to articulate and model problems given an understating of representational issues and abstraction in machine learning.
  • Be able to explain and analyze models and results making use of theoretical principles and the limitations of generalization in machine learning.
  • Make use of the algorithmic theory of machine learning in problem analysis and model selection.
  • Understand and apply the maximum likelihood principle and explain algorithmic implications in modeling and problem solving.
  • Be able to use a variety of algorithmic techniques in machine learning.
  • Be able to choose and use a variety of machine learning protocols in different situations.


Grading Policy:

Letter Grade Marks Grade Point
A 90 - 100 4.00
A- 85 - 89 3.70
B+ 80 - 84 3.30
B 75 - 79 3.00
B- 70 - 74 2.70
C+ 65 - 69 2.30
C 60 - 64 2.00
C- 55 - 59 1.70
D+ 50 - 54 1.30
D 45 - 49 1.00
F 00 - 44 0.00