DAMP Physics, IITB

MA 5102: Basic Algebraic Topology (2019-20) Course Instructor:  Ronnie Sebastian Course Name:  Basic Algebraic Topology Credits:  6 Course Type: Elective Prerequisites:  General Topology Course Content:   Covering Maps and Lifting Theorems, The Fundamental Group (including Van Kampen theorem), Galois Correspondence for covering maps, Homotopy equivalences and Mapping Cylinders, Singular Homology  Other Topics Covered:  Higher Fundamental Groups (covered in tutorials) Books:  Hatcher, Professor has his own notes online here: http://www.math.iitb.ac.in/~ronnie/Fall2019/Lecture-Notes.pdf Lectures:   No Attendance Policy (but he doesn't appreciate it when you skip classes), blackboard lectures. I find Prof. Ronnie to be more concerned about getting the overall picture about proofs and other things across rather than caring about trivial details. The quality of lectures was good however, it was difficult to follow courses if you ...

PH 217: Classical Mechanics (2019-20) Course Instructor:  Prof. Sumiran Pujari Course Name:  Classical Mechanics Credits:  6 Course Type:  Core Prerequisites:  Informal: Linear Algebra,  Calculus Formal: None Course Content:   D'Alembert's Principle Lagrange's Equations Hamilton's principle and Calculus of Variations Lagrange Multipliers Conservation Theorems and Symmetry  Small oscillations - normal modes, forced vibrations, damped oscillation, response function Legendre transformations and Hamilton's equations of Motion Canonical Transformations, Symplectic Approach and Poisson brackets Liouville's theorem Central Force - Classification of orbits, Kepler Problem, Scattering in Central Force Field Other Topics Covered:  Infinitesimal Canonical Transformations (not included in the exam syllabus) Books:  Classical Mechanics - Goldstein et al. Mechanics - Landau and Lifshitz Lectures:   No attendance was ...

GNR 638: Machine Learning for Remote Sensing- 2 (2018-19) Course Instructor:  Prof. Biplab Banerjee Course Name:  Machine Learning for Remote Sensing- 2 Credits:  6 Course Type:  Elective Prerequisites: None Course Content:  Basics of Machine Learning, classical Image processing techniques, Convolution neural networks, Recurrent neural networks, Autoencoders and their variants, Domain Adaptation, Generative Adversarial Networks Books:  I found online blogs (medium, towardsdatascience) more useful. Lectures:  Lecture slides are available. Prof follows institute attendance policy.  Assignments:   The assignments are fairly simple and interesting. Exams:  No quizzes are held.  Online Study Materials / websites you found useful for the course :  Medium, towardsdatascience, etc   Pro Tips:  Do the assignments sincerely Respondent:  Ruchika Chavhan

PH567 Non Linear Dynamics (Autumn 19-20) Course Instructor:  Punit Parmananda Course Name:  Non Linear Dynamics Credits:  6 Course Type:  Elective Prerequisites:  No prerequisites Course Content:  Damped oscillators, logistic equation and bifurcation map, stability of systems and Jacobian, chaos and experimental techniques Books:  Strogatz Lectures:   The professor gives a lot of importance to attendence, participation in class. Lectures are pretty informal, taught on the board and with slides. Assignments:   Problems are discussed in class, no formal tutorial schedule Exams:  Two quizzes, one midsem and one endsem. Also a presentation worth 20% marks. Online Study Material:  Wikipedia if you want, but strogatz is pretty much all you need Advanced Follow-up Courses:  - Pro Tips:  Try to actively participate in discussions and doubts in the class. Personal Comments:  You've surely not ...

Course Instructor:  S. Shankaranarayanan Course Name:  Quantum Mechanics-2 Credits:  6 Course Type:  Core Prerequisites: QM-1 Course Content:  Brief review of QM-1, Symmetries in QM, Clebsch Gordon coefficients and addition of angular momentum, Perturbation theory (time-independent and time-dependent), WKB method, scattering Books:  Modern Quantum Mechanics by J.J Sakurai, Introduction to Quantum Mechanics by D. Griffiths  Lectures:   Attendance-Fairly strict attendance policy. DX grades were given.  Lectures- no slides. Lectures were quite good, but sometimes a little rushed or hard to follow. The profs handwritten notes were routinely uploaded and were a good reference in addition to the mentioned texts. Assignments:   Assignments: Weekly/bi-monthly assignments. Questions were mostly straightforward, but each assignment had one or two challenging problems. Students were expected to solve all tutorial problems b...

Course Instructor:  S. Baskar Course Name:  Introduction to Probability Theory Credits:  8 Course Type:  Minor Prerequisites:  None Course Content:  Axioms of probability theory, Countable and uncountable sample spaces and their sigma fields, properties of probability measure, de Morgan's laws, inclusion-exclusion principle, conditional probability, Bayes' theorem, Random variables and random vectors and their distribution and density functions, expectations and moments, moment generating functions, convergence of sequence of random variables, proofs of limit theorems. Books:  Hael, Port and Stone  Lectures:  Strict Attendance policy, teaching on blackboard Assignments:   Strong emphasis on proofs and theory. Numerical problems were easy. Problems required rigorous proofs and justification Advanced Follow-up Courses:  SI402 Statistical Inference, SI404 Applied Stochastic Processes, SI527 Introduction to ...