Courses I have taught
Here is a list of courses I have taught at Northeastern University. The main purpose is to help students to register for courses. Here are only the lecture notes and syllabi but NOT the full teaching materials. Ongoing class websites are on Canvas at Northeastern including assignments, computer labs, exams, final projects, etc.
Graduate Courses:
MATH 5110: Applied Linear Algebra and Matrix Analysis.
This course provides a rigorous treatment of the concepts and computational techniques of linear algebra
MATH 7339: Machine Learning and Statistical Learning Theory 2
Continues Math 7243. The course further covers theory and methods for regression and classification, along with other advanced topics in machine learning, statistics and deep learning.
Math 7978–Independent Study
Offers independent work under the direction of members of the department on a chosen topic.
MATH7243: Machine Learning and Statistical Learning Theory 1
Introduces both the mathematical and statistical theory of learning and the implementation of modern machinelearning algorithms appropriate for data science.
MATH 7741 Readings in Probability and Statistics
Offers a reading course to be arranged between an individual student and instructor on a topic of their mutual choice.
Rational Homotopy Theory
A summer course on rational homotopy theory at University of Nevada, Reno.
Undergraduate Courses:
MATH 4570: Matrix Methods in Data Analysis and Machine Learning
This course provides a rigorous treatment of the concepts and computational techniques of linear algebra and explicit explanations of the mathematics behind machine learning and neural network.
MATH 2331: Linear Algebra
The course features the basic theory of Linear Algebra, including Matrices, solving linear systems using Gauss-Jordan elimination, determinants, vector spaces, eigenvalues and eigenspaces and application to discrete dynamical systems, and the theory of orthogonal & symmetric matrices, SVD.
MATH 182 : Calculus 2
Topics Covered: Substitution rule, Area between the curves, Volumes, Improper integrals, Sequences, Series, Integral test, Comparison, Alternating and Ratio tests, Power Series, Taylor and Maclaurin Series, Work, Arc Length, Differential and Separable Equations, Euler’s Method, Integration By Parts, Trigonometric Substitutions for Integrals, Integration of Rational functions by using partial fractions, Approximate Integration.
Math 3081: Probability and Statistics
This course provide the theory of Calculus-based probability and statistics and develop the mathematical tools and concepts necessary for modeling uncertainty and data analysis in real-world problem
Math 2321- Calculus 3 for Science and Engineering
Extends the techniques of calculus to functions of several variables; introduces vector fields and vector calculus in two and three dimensions. Topics include lines and planes, 3D graphing, partial derivatives, the gradient, tangent planes and local linearization, optimization, multiple integrals, line and surface integrals, the divergence theorem, and theorems of Green and Stokes with applications to science and engineering and several computer lab projects.
Math1231: Calculus for Bussiness and Economics
This course introduces students to the use of derivatives and integrals in solving problems in business and economics, e.g., maximizing profit, calculating average investment income and future value of an income stream.