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| title: EC 101 | ||
| description: Core | ||
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| <header class="major"> | ||
| <h2>EC101: Economics (2023) Autumn | ||
| <br>Saptarshi Ghosh – Microeconomics | ||
| Aditi Chaubal - Macroeconomics</h2> | ||
| </header> | ||
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| <h4> Prerequisite</h4> | ||
| <p>None</p> | ||
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| <h4> Course Content</h4> | ||
| <p>Basic economic problems. resource constraints and Welfare maximizations. Nature of Economics : Positive and normative economics; Micro and macroeconomics, Basic concepts in economics. The role of the State in economic activity; market and government failures; New Economic Policy in India.Theory of utility and consumer"s choice. Theories of demand, supply and market equilibrium. Theories of firm, production and costs. Market structures. Perfect and imperfect competition, oligopoly, monopoly.An overview of macroeconomics, measurement and determination of national income. Consumption, savings, and investments. Commercial and central banking. Relationship between money, output and prices. Inflation - causes, consequences and remedies. International trade, foreign exchange and balace payments, stabilization policies : Monetary, Fiscal and Exchange rate policies.</p> | ||
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| <h4> Books</h4> | ||
| <p>P. A. Samuelson & W. D. nordhaus, Economics, McGraw Hill, NY, 1995.A. Koutsoyiannis, Modern Microeconomics, Macmillan, 1975.R. Pindyck and D. L. Rubinfeld, Microeconomics, Macmillan publishing company, NY, 1989.R. J. Gordon, Macroeconomics 4th edition, Little Brown and Co., Boston, 1987.William F. Shughart II, The Organization of Industry, Richard D. Irwin, Illinois, 1990. | ||
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| <h3> Review by Anonymous </h3> | ||
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| <h4>Lectures</h4> | ||
| <p>Slides were not super coherent and easy to understand. You could still memorise the contents and do well in the exam, but the capacity to do that depends on the person.</p> | ||
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| <h4> Assignments, Exams and Grading</h4> | ||
| <p>There were 2 quizzes (10% each) apart from the midsem and endsem(40% each. Macro – Midsem, Micro – Endsem). Micro and Macro prof.s had a different style of evaluation from each other. In the first half sem (Macro), the quizzes had negative marking, but in 2nd half, there were just a lot more questions with more memorization required and also attention to what was taught in class, because there was at least some thing in the Micro exam which wasn't there in the slides (prof. dependent). Attendance had no marks. Grading took place separately for all divisions. They had different prof.s and also slightly different curricula (though, they were same for the most part). No cheat sheets were allowed in the exam, as this course was mostly about memorisation. | ||
| </p> | ||
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| <h4> Tips</h4> | ||
| <p>"Principles of Economics" by Gregory N. Mankiw (for Macro). | ||
| "Samuelson Paul A, William D. Nordhaus" , Economics (for Micro). | ||
| Preferably read the book little by little, on a regular basis.</p> | ||
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| title: MM225 PH XXX | ||
| description: Core | ||
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| <h2>MM225 (PH XXX): AI and Data Science (2023) Autumn | ||
| <br>M.P. Gururajan - Probability (Meta Dept) | ||
| Hina Gokhale – Statistics (Meta Dept) | ||
| </h2> | ||
| </header> | ||
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| <h4> Prerequisite</h4> | ||
| <p>None</p> | ||
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| <h4> Course Content</h4> | ||
| <p>1. Programming Basics (Python programming, basic data structures in Python, Data handling, Introduction to data file i/o, Introduction to n-d arrays (numpy), Introduction to plotting (25%) 2. Introduction to Probability (25%). Sample Spaces and events Probability axioms. Properties of Probability, Counting Techniques. Random Variables. Expectations and Variances. Visualizing PDF: Point plot, PDF, CDF, histogram, binning issues in histogram. Conditional probabilities and conditional expectation. Independence. Important discrete and continuous distributions. Bivariate distributions. Visualization of relationship between two variables: bi-variate histogram, conditional PDFs. Joint Probability distributions. Multivariate Normal Distributions with the corresponding mean vectors, variance-covariance matrices and correlation matrices. 3. Hypothesis testing (5%). Type 1 and Type 2 errors. Testing for parameters of a normal distribution and for percentages based on a single sample and based on two samples. Introduction to the chi-squared test. The concept of p-value. 4. Exploratory data analysis and data visualization: Unsupervised data exploration methods: PCA, SVD, T-SNE, etc (10%) 5. Introduction to supervised learning (25%) What is learning, learning objectives. Training, validation, and testing. General linear regression with testing hypothesis for regression coefficients and model ANOVA, Comparing the performance and tests using one way / multiple way ANOVA, Classification and regression, Neural networks, CNNs. 6. Department-specific applications (10%)</p> | ||
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| <h4> Books</h4> | ||
| <p>Principles and Techniques of Data Science, By Sam Lau, Joey Gonzalez, andDeb Nolan, 2019, available online at https://www.textbook.ds100.org/intro● Python for data analysis, Wes Mckinney, O Reilly, 2013● CUDA by Example: An Introduction to General-Purpose GPU Programming,Jason Sanders, Nvidia, 2010● NORMAN MATLOFF. Parallel Computing for Data Science: With Examples in R,C++, and CUDA. Boca Raton: CRC Press.● Pattern Recognition and Machine Learning, by Christopher Bishop, Springer 2011● The Elements of Statistical Learning: Data Mining, Inference, and Prediction,Second Edition, by Trevor Hastie and Robert Tibshirani (Springer Series inStatistics) 2016● Dive into Deep Learning by Aston Zhang, Zack C. Lipton, Mu Li and AlexanderSmola, 2020 (https://d2l.ai)● Deep Learning, I. GoodFellow, Y. Benjio and A. Courville, MIT Press, 2017.● Introduction to Probability and Statistics for Engineers and Scientists 5th Editionby Sheldon M. Ross (Author)● Mathematics for machine learning. Mark Deisenroth et. al., Cambridge Press,2021. | ||
| </p> | ||
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| <h3> Review by Anonymous </h3> | ||
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| <h4>Lectures</h4> | ||
| <p>The biggest trouble was that after just one semester of a coding course (CS101) , everyone was expected to be good enough at coding to be able to tackle the labs without issue. After a couple of labs, they would just provide the problem name and students would have to prepare what they could, given the name of the topic. | ||
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| The course wasn't well organized and well taught at all as it was put together in a very short amount of time. Frequent reliance on online resources for understanding course content was necessary. Content (particularly of first half sem) was nearly impossible to understand from the slides and pretty difficult to understand from the books)</p> | ||
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| <h4> Assignments, Exams and Grading</h4> | ||
| <p>Weekly Labs (30%), two quizzes (15% each), Midsem (15%), Endsem (40% (full syllabus)). Each question carried very little individual weightage up until Endsem (where each question was worth 5% of the total, and there were 8 questions). There was no attendance. | ||
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| Though it must be kept in mind that there might be a significant difference between the new AI/DS course and MM225 – The professors will be different and the course won't take place in an LA with multiple departments. | ||
| </p> | ||
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| <h4> Tips</h4> | ||
| <p>"Introduction to Probability" by Grinstead and Snell (first half sem) | ||
| "Introduction to Probability and Statistics for Engineers and Scientists" by Sheldon M. Ross (second half sem) | ||
| Truthfully speaking, these books aren't particularly useful either. They were just the source material. Online resources(like videos), even Wikipedia, were much better most of the time.</p> | ||
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| title: PH 113 | ||
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| <h2>PH 113: Oscillations and Waves (2022) Spring | ||
| <br>Prof P. Ramadevi</h2> | ||
| </header> | ||
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| <h4> Prerequisite</h4> | ||
| <p>None</p> | ||
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| <h4> Course Content</h4> | ||
| <p>Simple Harmonic motion, damped SHM, critical damping, Sustaining oscillations in a damped oscillator. Driven oscillation, resonance, damped-driven oscillator and its resonance, Q-factor, Vanderpol oscillator, non-linear feedback for sustained oscillations. SHM in 2-dim, dependence on initial conditions, Lissajous figures, condition for closed orbits, SHM in 3-dim. Oscillations of two particle systems, symmetric and asymmetric modes, general solution to the problem. Driven oscillations of two particle system. Oscillations of `n` particle systems, normal modes, Formulation of the general problem, eigenvalues and eigenvectors of normal modes, general solution for arbitrary initial conditions. Driven oscillations. Example of a linear triatomic molecule. Longitudinal and transverse oscillations, modding out the zero frequencies. Oscillations of a chain of `n` atoms. Continuum limit, vibrational modes of a string of constant density. Equation of Motion for waves, Standing waves and travelling waves in 1 dimensions. Properties of waves in two and three dimensions Harmonics, Linear superposition of harmonics, odd harmonics, construction of pulse shapes. Fourier components of a periodic pulse, Fourier analysis and Fourier coefficients. Fourier analysis of arbitrary functions, Fourier Coefficients.</p> | ||
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| <h4> Books</h4> | ||
| <p>Berkeley Physics Course (Vol 3): Waves by Frank S. Crawford | ||
| </p> | ||
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| <h3> Review by Gurupoorna </h3> | ||
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| <h4>Lectures</h4> | ||
| <p>Moderate level of difficulty. Since it was a half-semester course in just the 2nd semester, the professor did not involve too much mathematical jargon into it. | ||
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| There were weekly assignments including good questions, some of which were also discussed in tutorial classes, conducted by the professor herself. Requires a bit of practice in solving those questions. | ||
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| The course was very interesting and much rather beautiful.</p> | ||
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| <h4> Assignments, Exams and Grading</h4> | ||
| <p>Two quizzes, a midsemester and an endsemester. The endsemester has a higher weightage than most theory courses. The exams contain all variety of questions, from easy to tricky. However, they do tend to be lengthy. Grading was average. | ||
| </p> | ||
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| <h4> Tips</h4> | ||
| <p>Moderate understanding of differential equations and linear algebra will guide better in concepts and lets you appreciate the subject. | ||
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| Resources that I used was Waves notes by David Morin.</p> | ||
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| title: PH 216 | ||
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| <h2>PH 216: Statistical Physics (2023) Spring | ||
| <br>Prof. Dibyendu Das</h2> | ||
| </header> | ||
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| <h4> Prerequisite</h4> | ||
| <p>None</p> | ||
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| <h4> Course Content</h4> | ||
| <p>(1) Basics of probability theory, moments, cumulants and Central limit theorem. (2) Thermodynamic equilibrium and stability, response functions and thermodynamic potentials. (3) Isolated system. Entropy and phase space volume. Micro-canonical ensemble. Ideal gas. (4) System and reservoirs. Canonical, Gibbs, and Grand Canonical ensembles. Partition function and thermodynamic connections. (5) Quantum Statistics and mixed states. Density matrix in position basis for single particle and N non-interacting Fermions and Bosons. (6) Fermi and Bose gases at High temperatures. (7) Fermi and Bose gases at low temperature, pressure, specific heat, and applications.</p> | ||
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| <h4> Books</h4> | ||
| <p>1) Statistical Physics of particles, by Mehran Kardar (Cambridge University Press, 2007).3022402) Statistical Mechanics, by R. K. Pathria (Butterworth-Heinemann, 1996).3022403) Statistical Mechanics, by Kerson Huang (John Wiley & sons, 1987). | ||
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| <h3> Review by Shanttanu Oberoi </h3> | ||
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| <h4>Lectures</h4> | ||
| <p>The lectures were very good and explainable; Prof. had already shared all the lecture notes on moodle, which were pretty similar to the things Prof. did in the class. You won't need to go to the lectures so far away, as the notes were self-explanatory for me. Try understanding notes on your own; if you don't understand them well, then I would suggest you attend every lecture; don't be lazy like me. I would also recommend viewing Kardar lectures on the mitocw</p> | ||
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| <h4> Assignments, Exams and Grading</h4> | ||
| <p>Quiz1- 15% | ||
| Mid-sem- 30% | ||
| Quiz2- 10% | ||
| End-sem- 45% | ||
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| All the exam questions were like that. You won't find any except 1-2 in any book. The professor handcrafted all the questions by himself. | ||
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| If you had practiced tutorials carefully, the questions wouldn't be too hard for you, the solutions of the questions sometimes become too lengthy and don't match any perfect value you were expecting or heard of, and 80% of the time your solution would be right in that case as many times you get the very messy result. Also don't target doing all the questions in the exam as time is very less comparable to the hardness of the question, try doing those questions first which you think you would be able to write neat and tidy solution | ||
| </p> | ||
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| <h4> Tips</h4> | ||
| <p>Soft Prereq- Complex Analysis, Differential Equations, Probability, Basic Quantum Mechanics | ||
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| You should have a strong grip on all the prerequisites as they are the foundations of this course. If your Prof. is Dibyendu Das, then there won't be any benefit of solving previous years' questions as they are completely irrelevant; instead, focus more on the tutorials. I hope you will love this course as it gives a mathematical picture of the world around mainly all the thermodynamic processes happening around us </p> | ||
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