CP 322

Machine Learning

100%Liked

Easy

40%

Useful

80%

1 ratings

With the rise of Data Science and Big Data, machine learning has been recognized as the key driver behind successful advances of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary topics in predictive models, including regressions, classifications, neural networks and deep learning, ensemble-based methods, performance evaluation, etc. In addition, the course provides case studies that describe specific data analytics projects, and provide opportunities to employ a variety of machine learning toolboxes. Pre-requisites: CP312.

100%Liked

Easy

40%

Useful

80%

1 ratings

With the rise of Data Science and Big Data, machine learning has been recognized as the key driver behind successful advances of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary topics in predictive models, including regressions, classifications, neural networks and deep learning, ensemble-based methods, performance evaluation, etc. In addition, the course provides case studies that describe specific data analytics projects, and provide opportunities to employ a variety of machine learning toolboxes. Pre-requisites: CP312.

CP 322

Machine Learning

100%Liked

Easy

40%

Useful

80%

1 ratings

With the rise of Data Science and Big Data, machine learning has been recognized as the key driver behind successful advances of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary topics in predictive models, including regressions, classifications, neural networks and deep learning, ensemble-based methods, performance evaluation, etc. In addition, the course provides case studies that describe specific data analytics projects, and provide opportunities to employ a variety of machine learning toolboxes. Pre-requisites: CP312.

100%Liked

Easy

40%

Useful

80%

1 ratings

With the rise of Data Science and Big Data, machine learning has been recognized as the key driver behind successful advances of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary topics in predictive models, including regressions, classifications, neural networks and deep learning, ensemble-based methods, performance evaluation, etc. In addition, the course provides case studies that describe specific data analytics projects, and provide opportunities to employ a variety of machine learning toolboxes. Pre-requisites: CP312.

Course Schedule

CRN	Type	Section	Campus	Enrolled	Time	Date	Location	Instructor
No Sections Found

CRN	Type	Section	Campus	Enrolled	Time	Date	Location	Instructor
4404	Lecture	B	Waterloo	0/150	1:00 PM - 2:20 PM	M T W Th F S Su
1816	Lecture	A	Waterloo	0/150	11:30 AM - 12:50 PM	M T W Th F S Su

CRN	Type	Section	Campus	Enrolled	Time	Date	Location	Instructor
1653	Lecture	C	Waterloo	0/80	2:30 PM - 3:50 PM	M T W Th F S Su
4301	Lecture	D	Waterloo	0/80	7:00 PM - 8:20 PM	M T W Th F S Su

CRN	Type	Section	Campus	Enrolled	Time	Date	Location	Instructor
No Sections Found

CP 322 Prerequisites

CP 312 (Min. Grade D-)

CP 322 Leads To

No Leads To Information Available

CP 322 Restrictions

Must be enrolled in one of the following Levels:

Undergraduate (UG)

What do you think of CP 322?

Course Reviews

A tough course to be sure, but the assignments in particular are very helpful to learn the application of machine learning and a few algorithms to get you started.

Easy

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Liked

— Data Science (BSc) student, taught by Azam Asilian Bidgoli