Category Archives: Course Reviews

Course Review: CPSC 311

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Definition of Programming Languages

“I prefer the word ‘thunk’ to expression closures.”

Text: Programming Languages: Application and Interpretation by Shriram Krishnamurthi

Prof: Dr. Steve Wolfman

Wolfman was slightly less flipped in CPSC311, especially as the term progressed. His lectures were pretty entertaining and included a lot of live modification of the interpreter. Instead of sticking to textbook examples of dynamic scope and laziness, we often worked on languages in assignments and midterms where exotic adaptations and variants of these concepts were incorporated into a language in mind-bending ways.

Difficulty

I expected this to be an easy course. It ended up taking a lot of time. A lot of the programming languages concepts were completely new to me and combined with Wolfman’s avant-garde presentation of the material, I spent a great deal of time preparing for midterms and understanding the readings. The project we selected was in Elixir, and it was also very time-consuming as we had to learn distributed systems concepts in a new language with little guidance. That said, I feel the project was kind of a ‘choose-your-on-adventure’. You could do very well in the project with far less work if you chose wisely initially. Overall, the average was quite hight (around 77%) even though the midterm averages were quite low.

Key Concepts

Scoping

Deferred evaluation

Recursion

Continuations

Functional programming

SKI combinator

Interpreters

 Hard Concepts

Dynamic scope: Really messes with your brain, helps to write out the context.

Continuations: Odd concept. Helps to think of what function the continuation is bound to and apply it.

SKI combinator: Takes practice to get used to ‘algebraic’ manipulations of functions.

Conclusion

Learnt a lot. Enjoyed the project. Challenging course.

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Course Review: CPSC 313

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Computer Hardware and Operating Systems

“Virtual memory is one of the greatest ideas in the history of computer science.”

Text: Computer Systems: A Programmer’s Perspective by Randal Bryant, David O’Hallaron 3rd Edition

Prof: Dr. Donald Acton

Dr. Acton is a nice person and a passionate lecturer. His assignments are interesting and he adds a historical angle to the material. Unfortunately, certain administrative mishaps greatly hindered the delivery of the course. If the readings and online quizzes for each week were clearly outlined, assignments were screened by TA’s for errors and more TA’s staffed with monitoring piazza the course would have been a lot more enjoyable.

Difficulty

After returning from a systems heavy co-op, I found the material much less intimidating then CPSC 213. The assignments were lengthy but very doable and had a high average. The quizzes were more challenging, but if you practiced the exercises from the book and practice quizzes you should be fine. Readings are really important to get the most out of this course, especially as the slides are not amenable to self-study.

Key Concepts

Pipelining

Caching

Virtual Memory

Locality

File Systems

Exceptions

 Hard Concepts

Calculating cache miss rate for C code: Need to clearly visualize the cache and how the data structure fits into it.

Assembly implementation: Need to remember how the stack and registers can be used to pass parameters and store stack pointers and return addresses.

Conclusion

Somewhat interesting course. I found it a lot more detailed and less mind-blowing then CPSC213.

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Course Review: MATH 302

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Introduction to Probability

“Donald claims that he won the popular vote if you subtract the 3 million illegal voters. Assuming that 3 million people did vote illegally, compute the probability that Donald is correct.”

Text: Introduction to Probability by David F. Anderson, Timo Seppalainen, and Benedek Valko

Prof:  Dr. Martin Lohmann

Dr. Lohmann’s lectures largely consisted of (usually) interesting examples. Some students found his accent and his handwriting a bit challenging to follow. This, combined with the fact that he defers posting of lecture notes, made the course harder than necessary for such students. I did not have any difficulty understanding what was being said, and the few times I found his handwriting became hard to read, he clarified immediately. Also, if you ask a stupid question, expect some deft sarcasm in response!

Difficulty

While I find probability counter-intuitive, the assignments were all quite doable. Occasionally, harder questions marked with a star were provided. The first midterm was quite tricky, however, we were given a practice midterm beforehand that was conceptually quite similar to the actual exam. The second midterm and final were both significantly easier and we also were given similar practice material.

Key Concepts

Counting

Sets

Discrete vs Continuous Probability Distributions

Mean, Variance and Covariance

Joint Distribution

Convergence in Probability/Distribution

Conditional Probability

Moment-generating functions

 Hard Concepts

Counting: I always make incorrect assumptions w/regard to counting. I think the key to easier problems is to identify whether you are using replacement/no replacement and order/no order. For harder problems, it is often necessary to construct a bijection of sorts or use a symmetry argument.

Conclusion

Good to get some practice with counting and probability. About as much theory as you would expect from such a class.

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Course Review: CPSC 320

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Intermediate Algorithm Design and Analysis

“In this course, we mostly just do a high-level analysis of algorithms which are heavily used in the real-world. Today, we do the opposite. We actually implement an algorithm that is utterly useless.”

Text: Algorithm Design by Jon Kleinberg and Eva Tardos

Prof:  Dr. Steve Wolfman

Wolfman is really entertaining. He is also a big believer in ‘flipped classroom’ approach, so the lectures are mostly problem-solving. I like the practice, but others may not like the lack of highly structured lectures and the necessity to do readings ahead of the class. He thinks deeply about his students’ education, so few pedagogical decisions are arbitrary though they may appear so.

Difficulty

The assignments were initially easy but became increasingly lengthy and time-consuming. A background with mathematical proofs might help. I found the level of rigor required for the proofs lower than in CPSC 221, but the concepts were definitely more complex. The midterms were closely linked to the assignments and tested generic problem-solving skills rather than accumulated knowledge (though you probably need to know your definitions and the high-level idea of important algorithms). The tutorial and pre-reading quizzes were hit and miss, but they were more for familiarizing yourself with the topic than an actual assessment.The final was a bit longer than I expected, but all the questions were definitely doable if I had ~20 more minutes.

Key Concepts

Reductions

Divide and Conquer

Greedy Algorithms

Dynamic Programming

 Hard Concepts

NP-Completeness: Some reductions are very sophisticated, but they rarely expect us to come up with complex reductions.

Counterexamples: Sometimes it can take a lot of time to come up with a counterexample. Sometimes it’s better to list some constraints your counter-example must satisfy before diving into a brute-force search.

Conclusion

Enjoyable, useful sequel to CPSC221.

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Course Review: MATH 320

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Real Variables I

“You don’t really need a metric. All you need is the open sets.”

Text: Principles of Mathematical Analysis by Walter Rudin (3rd Edition)

Prof:  Dr Joshua Zahl

Dr Zahl was a structured, clear professor. He also provided useful insights into the ideas behind various proofs. I don’t think he has taught this course many times before, so he is probably still in the process of fine-tuning his delivery. Also, his surname literally means ‘number’ in German!

Difficulty

While the readings are pretty dense, the homework was quite doable by honours mathematics standards. I found the first midterm surprisingly easy. The second midterm was significantly harder, and I had not fully understood the concept of compact sets, so I did not do well at all. The class also got wrecked so there was a lot of scaling. The final was very reasonable.

Key Concepts

Properties of Real Numbers

Metric Spaces

Open Sets

Sequences

Continuous Functions

The Derivative

 Hard Concepts

Compact Sets: This concept has a deceptively Byzantine definition but is actually really fundamental to the course. Reading the history of the concept from Wikipedia and understanding many examples/counter-examples of sets that are or are not compact gave me a better intuition.

Sequences: Not a hard concept to understand, however an invaluable tool in certain seemingly intractable problems. Many times, it’s helpful to construct a sequence of points or even intervals and then use properties of such sequences in that space to prove the theorem.

Conclusion

Tough though doable class, if you have any background in mathematical proofs. One of the key learnings I took out this class, was the importance of spending time understanding complex definitions.

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Course Review: MATH 316

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Elementary Differential Equations II

“Even after that manipulation we still have some work left. We can’t get away with no work, we are not politicians.”

Text: Diffy Qs: Differential Equations for Engineer by J. Lebl

Prof:  Dr. Ian Frigaard

Dr. Frigaard was a humorous prof who had a lot of practical insights from an engineering perspective. The teaching was largely focused on examples, though a bit of theory was chucked in now and then for good measure. He was willing to humor a theoretical question if he had a bit of time.

Difficulty

I took it in the summer, so the material was probably simplified. The material is mainly straight-forward: we mostly practice lengthy manipulations involving Fourier Series. We had a quiz a week after the first week and he said he chucked the lowest quiz. All homework was optional, which was good because the homework was really lengthy (I am guessing the homework is mandatory during winter). While the quizzes generally tested basic questions on each topic, the final had slightly more complex questions.

Key Concepts

Fourier Series

Partial Differential Equations

Series Solutions

Separation of Variables

 Hard Concepts

D’Alembert’s Solution: Requires drawing of graphs, something I am not very efficient at.

Messy Algebra: The algebra on the final was a mess, with horrid fractions in each question, so get some practice.

Sturm-Liouville Theory: Slightly confusing until you realize you are not trying to solve the general Sturm-Liouville problem.

Conclusion

Very example heavy class. Mainly learned about 5 equations which had very similar solutions involving Fourier series and separation of variables. Would prefer a class where we learn more techniques and are exposed to a wide variety of equations instead. The algebra is also really gross.

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Course Review: CHIN 101

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Basic Chinese I: Part 1 (Non-Heritage)

“bā lā kè  ào bā mǎ (Barack Obama)!”

Text: Integrated Chinese: Simplified Characters Textbook & Workbook, Level 1, Part 1 by  Yuehua Liu

Prof:  Lee (An-Yi) Laoshi

Lee Laoshi is super funny and passionate. The class was the most enjoyable class I took that term because of interactive nature of the lessons. It was the only class that I didn’t occasionally look at the clock to see when it was ending. She was also very helpful and understanding. Unfortunately, she was only hired as a Visiting Lecturer and will be leaving UBC soon.

Difficulty

The workload was a lot. Weekly quizzes, character sheets, and the occasional workbook chapter created an onslaught of homework, even when there wasn’t a midterm. Most of it was memorization, so it was just a matter of spending the time. Most of the stuff was on the computer. Unfortunately, my computer packed up on the final, so I didn’t do as well as I did during the rest of the course and I lost a letter grade.

Key Concepts

Pinyin (pronunciation) with tones

Character reading

Character writing

Understanding oral Chinese

 Hard Concepts

 

Recognising pinyin orally: Really hard, be sure to get a lot of practice before the final. I advise recruiting a first language speaker friend to help out.

Writing characters: Quite painstaking, especially if you are not the best artist. Lots of practice.

Conclusion

Fun classes, exhausting homework and worthwhile introduction to one of the world’s most widely spoken languages.

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Course Review: MATH 215

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Elementary Differential Equations I

“UBC is a very progressive place…Because you get to learn Linear Algebra before Differential Equations!”

Text: Notes on Diffy Qs: Differential Equations for Engineers, by Jiri Lebl

Prof: Dr Dan Coombs

Dr Dan Coombs has great British accent, and a wry sense of humour which helps to keep interest in the class. He tries to balance between tolerating conceptual questions and making progress in the more recipe-oriented curriculum. He spent a lot of effort restructuring the curriculum to be based on Linear Algebra, so as to make the class more conceptual and slightly less “formula-up-my-sleeve” math, though it still is.

Difficulty

The homework is really exhausting. The hand calculations have awful numbers in them, making them really tedious. The Matlab is … Matlab. As a CS student I thought Matlab would be a breeze, but that was not the case, as the language has a lot of quirks. The number of questions in a homework set is a lot considering the time each one takes. With the exception of the first homework, where we were given real world problems and had to come with models for them, I didn’t feel I got a lot out of the homework, except learning a few random facts about Matlab after trial and error.

Key Concepts

Modelling nature as a differential equation

First order linear equations

Linear systems of differential equations

Laplace transform

Non-linear systems

 Hard Concepts

Partial fractions: Thought they were pretty easy, but had a really gross one on the final

Non-linear classification of fixed points: Can get a bit confused between different fixed points

Classification of 2nd order linear systems: If you don’t want to re-derive them, need to be able to recall them quickly.

Conclusion

Homework was a schlep. Interesting topic, but recipe-driven curriculum almost kills it. IMHO, focus should be modelling natural phenomenon. The problem with the recipe driven approach, even for non-math students, is that (1) Engineers will probably just use Wolfram/computer system to solve it anyway. (2) While it might be helpful for them to classify what can/cannot be solved etc, odds are if it is non-linear you will try your luck, or use a linear approximation anyway.

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Course Review: MATH 227

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Advanced Calculus II

“Consider an infinitesimal paddle wheel…”

Text: (none)

Prof: Dr. Joel Feldman

Dr. Joel Feldman strikes a great balance between being really organized, while still pretty relaxed. He is very helpful, in that he is always available between classes for questions. He is also really knowledgeable about the field (as he is a mathematical physicist) which is great, though he did give us a particularly tragic expression when we said we didn’t know what curl was, halfway through the course and he had to explain it.

Difficulty

The weekly assignments are generally all straightforward. The questions vary from computational to small proofs. I missed the first midterm, but a lot of people got wrecked, and it looked tough so watch out. Each question in the second midterm was manageable, though it required thinking. The challenge is that there are only four questions, so the cost of getting one question completely wrong is quite high. The final was similar except it had more questions. The challenge then was that often it was not clear which of the various techniques we had learned in the course was the correct approach to a question.

Key Concepts

Analysing/ parameterizing curves in 2,3-space

Analysing/ parameterizing surfaces in 3-space

Analysing/ parameterizing vector fields in 3-space

Integrating over curves, vector fields, and surfaces

Integral Theorems

 Hard Concepts

 

Applications of integral theorems: Hard to pick the right strategy that is going to work.

Biot-Savart Law: Really abstract, not sure if I understood it.

Conclusion

Feels like a fun physics course. Wish we had more time to discuss differential forms, but other than that pretty interesting class.

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Course Review: CPSC 221

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Basic Algorithms and Data Structures

“After 221 all of you should use “divide-and-conquer” when handing out exams, its a lot more efficient!”

Text: Objects, Abstraction, Data Structures, and Design Using C++ by Koffman, Elliot B., and Wolfgang, Paul

Prof: Dr. Alan Hu

Dr. Alan Hu is great at making everyday metaphors (often involving Justin Bieber) out of abstract computer concepts. He is also very patient in class, answering questions in detail. His classes have a slightly “free-wheeling” style because of this, so the lack of structure could distract some. Dr. Hu is a super approachable guy, (except during exams!), and he seems genuinely passionate about teaching computer science.

Difficulty

The assignments and labs are all do-able, though the penalties for small compilation errors on assignments are harsh. The midterm was pretty long and it had a mistake on it and I (and many others) got wrecked. The final was significantly easier and the scaling was huge. There is a lot of material in the course though, and the questions on exams definitely require some thinking. However, if you are okay with mathematical proofs and work consistently you should be okay after scaling.

Key Concepts

Big-O, Theta, Omega (Time and Space Complexity)

Sorting Algorithms

Basic Data Structures

Iteration and Recursion

Basic Graph Theory

Parallelism

 Hard Concepts

Proof of program correctness: Slightly different than mathematical proof, make sure to fully understand the conditions for a program to be correct. I only realized towards the end of the course, that this requires you to really read and understand the code fully.

Implementation in C++: Algorithms can look a lot less elegant in C++, so one needs to be familiar with common coding style in that language to interpret algorithms well.

Evaluating time complexity of given algorithm: Generally easy, but curve-balls can be thrown. Try breaking it down or stepping through the code.

Conclusion

Really important course for interviews, (along with 213). Felt like I improved a lot of reading code, and Dr. Hu was pretty entertaining.

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