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A summary of topics

  • Programming primitives
  • Derived programming structures
  • Programming-language concepts, design, and implementation
  • Programming “Paradigms”
  • Software engineering
  • Analysis
  • Side excursions

Programming Primitives

  • Recursion: the all-encompassing repetitive construct; recursive think- ing
  • Pairs: A universal data-structuring tool.
  • Functions as data values, functions on functions
  • Exceptions: Dealing with errors.
  • Classes

Derived Programming Structures

  • Can build almost anything from primitives.
  • Although Python also has specialized implementations of some important data structures.
  • Sequences:
    • Lists: traversals, searching, inserting, deleting (destructive and non-destructive)
    • Trees: traversals, binary search trees, constructing, inserting, deleting
  • Maps.
  • Iterators, generators
  • Trees: uses, traversing, and searching.

Programming Language Concepts, Design, Implementation

  • Python was developed largely as a teaching language, and is simpler in many ways than other “production” languages...
  • And yet, it is a good deal more powerful (as measured by work done per line of code) than these same languages.
  • Still, as you’ve seen, there are problems, too: dynamic vs. static discovery of errors.
  • Big item: scope (what instance of what definition applies to evalu- ation of an identifier). This is what environment diagrams are in- tended to model.
    • – Alternative: dynamic scoping.
  • Implementing a language [CS164]:
    • Interpreters
    • Trees as an intermediate language
    • Relationship of run-time environment representation to scope rules.
    • “Little” languages as a programming tool


  • Functional programming: expressions, not statements; no side-effects; use of higher-order functions.
  • Data-directed and object-oriented programming:
    • Organize program around types of data, not functions
    • Inheritance
    • Interface vs. implementation
  • Declarative programming:
    • State goals or properties of the solution rather than procedures.
    • Regular Expressions: Describe text with patterns; system figures out how to match them.
    • BNF: Describe languages with simple rules; system figures out how to parse them.
    • Syntax-Driven Translation: Hook BNF with rules that produce results. We saw calculators, language translators.

Software Engineering

  • Biggest ideas: Abstraction, separation of concerns
  • Specification of a program vs. its implementation
    • Syntactic spec (header) vs. semantic spec (comment).
    • Example of multiple implementations for the same abstract behavior
  • Testing: for every program, there is a test.
    • In “Extreme Programming” there is a test for every module.
  • Software engineering implicit in all our software courses, explicit in CS169.


What we can measure when we measure speed:

  • Raw time.
  • Counts of selected representative operations.
  • Symbolic expressions of running time.
  • Looking at worst cases simplifies the problem (and is useful).

Application of asymptotic notation (Θ(·), etc.) to summarizing symbolic time measurements concisely.

Side excursions

  • Computability [CS172]: Some functions cannot be computed. Problems that are “near” such functions often cannot be computed quickly.
  • SQL [CS186]: A widely used language for accessing and updating databases.
  • Prolog: A somewhat extreme example of a declarative programming language involving logical inference.

What's next?

What’s Next (Course-Wise)?

  • CS61B: (conventional) data structures, statically typed production languages.
  • CS61C: computing architecture and hardware as programmers see it.
  • CS70: Discrete Math and Probablilty Theory.
  • CSC100: Data Science
  • CS170, CS171, CS172, CS174: “Theory”—analysis and construction of algorithms, cryptography, computability, complexity, combinatorics, use of probabilistic algorithms and analysis.
  • CS161: Security
  • CS162: Operating systems.
  • CS164: Implementation of programming languages
  • CS168: Introduction to the Internet
  • CS160, CS169: User interfaces, software engineering
  • CS176: Computational Biology

What’s Next (Course-Wise)?

  • CS182, CS188, CS189: Neural networks, Artificial intelligence, Machine Learning
  • CS184: Graphics
  • CS186: Databases
  • CS191: Quantum Computing
  • CS195: Social Implications of Computing
  • EECS 16A, 16B: Designing Information Systems and Devices
  • EECS 126: Probabilty and Random Processes
  • EECS149: Embedded Systems
  • EECS 151: Digital Design
  • CS194: Special topics. (E.g.) computational photography and image manipulation, cryptography, cyberwar.
  • Plus graduate courses on these subjects and more.
  • And please don’t forget CS199 and research projects.

There’s Also Electrical Engineering

  • EE105: Microelectronic Devices and Circuits.
  • EE106: Robotics
  • EE118, EE134: Optical Engineering, Photovotalaic Devices.
  • EE120: Signals and Systems.
  • EE123: Digital Signal Processing.
  • EE126: Probability and Random Processes.
  • EE130: Integrated Circuit Devices.
  • EE137A: Power Circuits.
  • EE140: Linear Integrated Circuits (analog circuits, amplifiers).
  • EE142: Integrated Circuits for Communication.
  • EE143: Microfabrication Technology.
  • EE147: Micromechanical Systems (MEMS).
  • EE192: Mechatronic Design.

What's next? (Otherwise)

  • Programming contests
  • Hackathons
  • More paradigms and languages: the web
  • The open-source world: Go out and build something!
  • Above all: Have fun!

Fun with Python 🎉 🐍

What can you do with Python?

Almost anything! Thanks to libraries!

  • Webapp backends (Flask, Django)
  • Web scraping (BeautifulSoup)
  • Natural Language Processing (NLTK)
  • Data analysis (Numpy, Pandas, Matplotlib)
  • Machine Learning (FastAi, PyTorch, Keras)
  • Scientific computing (SciPy)
  • Games (Pygame)
  • Procedural generation - L Systems, Noise, Markov

*Except you should be careful when you use recursion...

Web scraping & Markov chains

👉🏽 Demo: Composing Gobbledygooks

Web scraping: Getting data from webpages by traversing the HTML.

Markov chain: A way to generate a sequence based on the probabalistic next token.

Markov diagram

Further learning: urllib2 module, BeautifulSoup docs, N-Gram modeling with Markov chains, CS70/EECS126 for Markov chains

Turtle & L-systems

👉🏽 Demo: L Trees!

Turtle: A library for drawing graphics (as if a pen is controlled by a turtle).

L-system: A parallel rewriting system and a type of formal grammar, developed originally by a biologist to model the growth of plants.

Example: Axiom: A, Rules: A → AB, B → A

                    n = 0 : A
                    n = 1 : AB
                    n = 2 : ABA
                    n = 3 : ABAAB

Further learning: turtle module, Tutorial: Turtles and Strings and L-Systems, Algorithmic Botany: Graphical Modeling using L-systems, L-system examples

Natural Language Processing

👉🏽 Demo: Sentence trees!

NLP includes language modeling, spelling correction, text classification, sentiment analysis, information retrieval, relation extraction, recommendation systems, translation question answering, word vectors, and more.

Further learning: NLTK Book, NLTK Sentiment Analysis, Dan Jurafsky's lectures and books, Berkeley classes: INFO 159, CS 288

Demo: Supervised Machine Learning

👉🏽 Demo: Bee vs. Wasp?

Diagram of supervised ML process

Further learning: FastAI Documentation, Kaggle ML tutorial, Bias in ML, Berkeley classes: CS182, CS188, CS189